Overview

Dataset statistics

Number of variables112
Number of observations5000
Missing cells52402
Missing cells (%)9.4%
Total size in memory4.3 MiB
Average record size in memory904.0 B

Variable types

Numeric81
Categorical28
Boolean3

Alerts

policy_code has constant value ""Constant
emp_title has a high cardinality: 3138 distinct valuesHigh cardinality
issue_d has a high cardinality: 115 distinct valuesHigh cardinality
url has a high cardinality: 5000 distinct valuesHigh cardinality
title has a high cardinality: 264 distinct valuesHigh cardinality
zip_code has a high cardinality: 711 distinct valuesHigh cardinality
earliest_cr_line has a high cardinality: 489 distinct valuesHigh cardinality
last_pymnt_d has a high cardinality: 91 distinct valuesHigh cardinality
last_credit_pull_d has a high cardinality: 80 distinct valuesHigh cardinality
loan_amnt is highly overall correlated with funded_amnt and 2 other fieldsHigh correlation
funded_amnt is highly overall correlated with loan_amnt and 2 other fieldsHigh correlation
funded_amnt_inv is highly overall correlated with loan_amnt and 2 other fieldsHigh correlation
installment is highly overall correlated with loan_amnt and 2 other fieldsHigh correlation
fico_range_low is highly overall correlated with fico_range_highHigh correlation
fico_range_high is highly overall correlated with fico_range_lowHigh correlation
mths_since_last_record is highly overall correlated with acc_now_delinq and 2 other fieldsHigh correlation
open_acc is highly overall correlated with num_satsHigh correlation
out_prncp is highly overall correlated with out_prncp_invHigh correlation
out_prncp_inv is highly overall correlated with out_prncpHigh correlation
total_pymnt is highly overall correlated with total_pymnt_inv and 1 other fieldsHigh correlation
total_pymnt_inv is highly overall correlated with total_pymnt and 1 other fieldsHigh correlation
total_rec_prncp is highly overall correlated with total_pymnt and 1 other fieldsHigh correlation
recoveries is highly overall correlated with collection_recovery_fee and 1 other fieldsHigh correlation
collection_recovery_fee is highly overall correlated with recoveries and 1 other fieldsHigh correlation
last_fico_range_high is highly overall correlated with last_fico_range_lowHigh correlation
last_fico_range_low is highly overall correlated with last_fico_range_highHigh correlation
mths_since_last_major_derog is highly overall correlated with next_pymnt_d and 1 other fieldsHigh correlation
tot_coll_amt is highly overall correlated with next_pymnt_dHigh correlation
tot_cur_bal is highly overall correlated with avg_cur_bal and 2 other fieldsHigh correlation
open_acc_6m is highly overall correlated with next_pymnt_dHigh correlation
open_act_il is highly overall correlated with next_pymnt_dHigh correlation
open_il_12m is highly overall correlated with next_pymnt_dHigh correlation
open_il_24m is highly overall correlated with next_pymnt_dHigh correlation
mths_since_rcnt_il is highly overall correlated with next_pymnt_dHigh correlation
total_bal_il is highly overall correlated with total_il_high_credit_limit and 1 other fieldsHigh correlation
il_util is highly overall correlated with next_pymnt_dHigh correlation
open_rv_12m is highly overall correlated with next_pymnt_dHigh correlation
open_rv_24m is highly overall correlated with next_pymnt_dHigh correlation
max_bal_bc is highly overall correlated with next_pymnt_dHigh correlation
all_util is highly overall correlated with next_pymnt_dHigh correlation
total_rev_hi_lim is highly overall correlated with next_pymnt_dHigh correlation
inq_fi is highly overall correlated with next_pymnt_dHigh correlation
total_cu_tl is highly overall correlated with next_pymnt_dHigh correlation
inq_last_12m is highly overall correlated with next_pymnt_dHigh correlation
acc_open_past_24mths is highly overall correlated with next_pymnt_dHigh correlation
avg_cur_bal is highly overall correlated with tot_cur_bal and 1 other fieldsHigh correlation
bc_open_to_buy is highly overall correlated with next_pymnt_dHigh correlation
bc_util is highly overall correlated with next_pymnt_dHigh correlation
mo_sin_old_il_acct is highly overall correlated with next_pymnt_dHigh correlation
mo_sin_old_rev_tl_op is highly overall correlated with next_pymnt_dHigh correlation
mo_sin_rcnt_rev_tl_op is highly overall correlated with next_pymnt_dHigh correlation
mo_sin_rcnt_tl is highly overall correlated with next_pymnt_dHigh correlation
mort_acc is highly overall correlated with next_pymnt_dHigh correlation
mths_since_recent_bc is highly overall correlated with next_pymnt_dHigh correlation
mths_since_recent_bc_dlq is highly overall correlated with next_pymnt_d and 1 other fieldsHigh correlation
mths_since_recent_inq is highly overall correlated with next_pymnt_dHigh correlation
mths_since_recent_revol_delinq is highly overall correlated with next_pymnt_d and 1 other fieldsHigh correlation
num_accts_ever_120_pd is highly overall correlated with next_pymnt_dHigh correlation
num_actv_bc_tl is highly overall correlated with next_pymnt_dHigh correlation
num_actv_rev_tl is highly overall correlated with num_rev_tl_bal_gt_0 and 1 other fieldsHigh correlation
num_bc_sats is highly overall correlated with next_pymnt_dHigh correlation
num_bc_tl is highly overall correlated with next_pymnt_dHigh correlation
num_il_tl is highly overall correlated with next_pymnt_dHigh correlation
num_op_rev_tl is highly overall correlated with next_pymnt_dHigh correlation
num_rev_accts is highly overall correlated with next_pymnt_dHigh correlation
num_rev_tl_bal_gt_0 is highly overall correlated with num_actv_rev_tl and 1 other fieldsHigh correlation
num_sats is highly overall correlated with open_acc and 1 other fieldsHigh correlation
num_tl_90g_dpd_24m is highly overall correlated with next_pymnt_dHigh correlation
num_tl_op_past_12m is highly overall correlated with next_pymnt_dHigh correlation
pct_tl_nvr_dlq is highly overall correlated with next_pymnt_dHigh correlation
percent_bc_gt_75 is highly overall correlated with next_pymnt_dHigh correlation
tot_hi_cred_lim is highly overall correlated with tot_cur_bal and 1 other fieldsHigh correlation
total_bal_ex_mort is highly overall correlated with next_pymnt_dHigh correlation
total_bc_limit is highly overall correlated with next_pymnt_dHigh correlation
total_il_high_credit_limit is highly overall correlated with total_bal_il and 1 other fieldsHigh correlation
grade is highly overall correlated with sub_gradeHigh correlation
sub_grade is highly overall correlated with gradeHigh correlation
loan_status is highly overall correlated with next_pymnt_dHigh correlation
last_pymnt_d is highly overall correlated with next_pymnt_dHigh correlation
next_pymnt_d is highly overall correlated with recoveries and 55 other fieldsHigh correlation
last_credit_pull_d is highly overall correlated with next_pymnt_dHigh correlation
acc_now_delinq is highly overall correlated with mths_since_last_recordHigh correlation
num_tl_120dpd_2m is highly overall correlated with mths_since_last_record and 3 other fieldsHigh correlation
num_tl_30dpd is highly overall correlated with mths_since_last_record and 2 other fieldsHigh correlation
pymnt_plan is highly imbalanced (99.3%)Imbalance
title is highly imbalanced (66.3%)Imbalance
next_pymnt_d is highly imbalanced (99.4%)Imbalance
last_credit_pull_d is highly imbalanced (54.6%)Imbalance
collections_12_mths_ex_med is highly imbalanced (90.3%)Imbalance
application_type is highly imbalanced (67.7%)Imbalance
acc_now_delinq is highly imbalanced (95.9%)Imbalance
chargeoff_within_12_mths is highly imbalanced (95.9%)Imbalance
num_tl_120dpd_2m is highly imbalanced (99.5%)Imbalance
num_tl_30dpd is highly imbalanced (97.3%)Imbalance
pub_rec_bankruptcies is highly imbalanced (75.4%)Imbalance
hardship_flag is highly imbalanced (99.3%)Imbalance
disbursement_method is highly imbalanced (79.6%)Imbalance
debt_settlement_flag is highly imbalanced (86.4%)Imbalance
emp_title has 398 (8.0%) missing valuesMissing
emp_length has 346 (6.9%) missing valuesMissing
title has 61 (1.2%) missing valuesMissing
mths_since_last_delinq has 2556 (51.1%) missing valuesMissing
mths_since_last_record has 4220 (84.4%) missing valuesMissing
next_pymnt_d has 2863 (57.3%) missing valuesMissing
mths_since_last_major_derog has 3688 (73.8%) missing valuesMissing
tot_coll_amt has 105 (2.1%) missing valuesMissing
tot_cur_bal has 105 (2.1%) missing valuesMissing
open_acc_6m has 1870 (37.4%) missing valuesMissing
open_act_il has 1870 (37.4%) missing valuesMissing
open_il_12m has 1870 (37.4%) missing valuesMissing
open_il_24m has 1870 (37.4%) missing valuesMissing
mths_since_rcnt_il has 1963 (39.3%) missing valuesMissing
total_bal_il has 1870 (37.4%) missing valuesMissing
il_util has 2328 (46.6%) missing valuesMissing
open_rv_12m has 1870 (37.4%) missing valuesMissing
open_rv_24m has 1870 (37.4%) missing valuesMissing
max_bal_bc has 1870 (37.4%) missing valuesMissing
all_util has 1871 (37.4%) missing valuesMissing
total_rev_hi_lim has 105 (2.1%) missing valuesMissing
inq_fi has 1870 (37.4%) missing valuesMissing
total_cu_tl has 1870 (37.4%) missing valuesMissing
inq_last_12m has 1870 (37.4%) missing valuesMissing
acc_open_past_24mths has 75 (1.5%) missing valuesMissing
avg_cur_bal has 105 (2.1%) missing valuesMissing
bc_open_to_buy has 123 (2.5%) missing valuesMissing
bc_util has 124 (2.5%) missing valuesMissing
mo_sin_old_il_acct has 249 (5.0%) missing valuesMissing
mo_sin_old_rev_tl_op has 105 (2.1%) missing valuesMissing
mo_sin_rcnt_rev_tl_op has 105 (2.1%) missing valuesMissing
mo_sin_rcnt_tl has 105 (2.1%) missing valuesMissing
mort_acc has 75 (1.5%) missing valuesMissing
mths_since_recent_bc has 116 (2.3%) missing valuesMissing
mths_since_recent_bc_dlq has 3854 (77.1%) missing valuesMissing
mths_since_recent_inq has 612 (12.2%) missing valuesMissing
mths_since_recent_revol_delinq has 3358 (67.2%) missing valuesMissing
num_accts_ever_120_pd has 105 (2.1%) missing valuesMissing
num_actv_bc_tl has 105 (2.1%) missing valuesMissing
num_actv_rev_tl has 105 (2.1%) missing valuesMissing
num_bc_sats has 89 (1.8%) missing valuesMissing
num_bc_tl has 105 (2.1%) missing valuesMissing
num_il_tl has 105 (2.1%) missing valuesMissing
num_op_rev_tl has 105 (2.1%) missing valuesMissing
num_rev_accts has 105 (2.1%) missing valuesMissing
num_rev_tl_bal_gt_0 has 105 (2.1%) missing valuesMissing
num_sats has 89 (1.8%) missing valuesMissing
num_tl_120dpd_2m has 281 (5.6%) missing valuesMissing
num_tl_30dpd has 105 (2.1%) missing valuesMissing
num_tl_90g_dpd_24m has 105 (2.1%) missing valuesMissing
num_tl_op_past_12m has 105 (2.1%) missing valuesMissing
pct_tl_nvr_dlq has 105 (2.1%) missing valuesMissing
percent_bc_gt_75 has 124 (2.5%) missing valuesMissing
tot_hi_cred_lim has 105 (2.1%) missing valuesMissing
total_bal_ex_mort has 75 (1.5%) missing valuesMissing
total_bc_limit has 75 (1.5%) missing valuesMissing
total_il_high_credit_limit has 105 (2.1%) missing valuesMissing
delinq_amnt is highly skewed (γ1 = 36.04017216)Skewed
url has unique valuesUnique
delinq_2yrs has 4067 (81.3%) zerosZeros
inq_last_6mths has 3053 (61.1%) zerosZeros
pub_rec has 4221 (84.4%) zerosZeros
out_prncp has 2872 (57.4%) zerosZeros
out_prncp_inv has 2872 (57.4%) zerosZeros
total_rec_late_fee has 4775 (95.5%) zerosZeros
recoveries has 4552 (91.0%) zerosZeros
collection_recovery_fee has 4567 (91.3%) zerosZeros
last_fico_range_low has 91 (1.8%) zerosZeros
tot_coll_amt has 4112 (82.2%) zerosZeros
open_acc_6m has 1399 (28.0%) zerosZeros
open_act_il has 390 (7.8%) zerosZeros
open_il_12m has 1736 (34.7%) zerosZeros
open_il_24m has 855 (17.1%) zerosZeros
total_bal_il has 372 (7.4%) zerosZeros
open_rv_12m has 1132 (22.6%) zerosZeros
open_rv_24m has 503 (10.1%) zerosZeros
max_bal_bc has 76 (1.5%) zerosZeros
inq_fi has 1542 (30.8%) zerosZeros
total_cu_tl has 1673 (33.5%) zerosZeros
inq_last_12m has 871 (17.4%) zerosZeros
acc_open_past_24mths has 227 (4.5%) zerosZeros
bc_open_to_buy has 76 (1.5%) zerosZeros
bc_util has 60 (1.2%) zerosZeros
delinq_amnt has 4985 (99.7%) zerosZeros
mo_sin_rcnt_rev_tl_op has 75 (1.5%) zerosZeros
mo_sin_rcnt_tl has 78 (1.6%) zerosZeros
mort_acc has 2081 (41.6%) zerosZeros
mths_since_recent_inq has 338 (6.8%) zerosZeros
num_accts_ever_120_pd has 3739 (74.8%) zerosZeros
num_actv_bc_tl has 102 (2.0%) zerosZeros
num_il_tl has 144 (2.9%) zerosZeros
num_tl_90g_dpd_24m has 4631 (92.6%) zerosZeros
num_tl_op_past_12m has 952 (19.0%) zerosZeros
percent_bc_gt_75 has 1349 (27.0%) zerosZeros
tax_liens has 4861 (97.2%) zerosZeros
total_il_high_credit_limit has 606 (12.1%) zerosZeros

Reproduction

Analysis started2023-07-21 11:17:09.955737
Analysis finished2023-07-21 11:17:32.290312
Duration22.33 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

loan_amnt
Real number (ℝ)

Distinct615
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15170.055
Minimum1000
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:32.361079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3500
Q18000
median13000
Q320000
95-th percentile35000
Maximum40000
Range39000
Interquartile range (IQR)12000

Descriptive statistics

Standard deviation9291.46814
Coefficient of variation (CV)0.6124874392
Kurtosis-0.1118949597
Mean15170.055
Median Absolute Deviation (MAD)6337.5
Skewness0.7894468495
Sum75850275
Variance86331380.2
MonotonicityNot monotonic
2023-07-21T13:17:32.464153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 411
 
8.2%
20000 291
 
5.8%
15000 267
 
5.3%
12000 257
 
5.1%
5000 203
 
4.1%
35000 201
 
4.0%
8000 175
 
3.5%
30000 155
 
3.1%
6000 148
 
3.0%
16000 145
 
2.9%
Other values (605) 2747
54.9%
ValueCountFrequency (%)
1000 24
0.5%
1100 1
 
< 0.1%
1125 1
 
< 0.1%
1200 4
 
0.1%
1250 1
 
< 0.1%
ValueCountFrequency (%)
40000 90
1.8%
39000 1
 
< 0.1%
38650 1
 
< 0.1%
38400 1
 
< 0.1%
38250 1
 
< 0.1%

funded_amnt
Real number (ℝ)

Distinct615
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15168.765
Minimum1000
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:32.570883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3500
Q18000
median13000
Q320000
95-th percentile35000
Maximum40000
Range39000
Interquartile range (IQR)12000

Descriptive statistics

Standard deviation9290.547298
Coefficient of variation (CV)0.6124788207
Kurtosis-0.1106932894
Mean15168.765
Median Absolute Deviation (MAD)6312.5
Skewness0.7898341448
Sum75843825
Variance86314269.1
MonotonicityNot monotonic
2023-07-21T13:17:32.670856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 411
 
8.2%
20000 291
 
5.8%
15000 267
 
5.3%
12000 257
 
5.1%
5000 203
 
4.1%
35000 201
 
4.0%
8000 175
 
3.5%
30000 155
 
3.1%
6000 148
 
3.0%
16000 145
 
2.9%
Other values (605) 2747
54.9%
ValueCountFrequency (%)
1000 24
0.5%
1100 1
 
< 0.1%
1125 1
 
< 0.1%
1200 4
 
0.1%
1250 1
 
< 0.1%
ValueCountFrequency (%)
40000 90
1.8%
39000 1
 
< 0.1%
38650 1
 
< 0.1%
38400 1
 
< 0.1%
38250 1
 
< 0.1%

funded_amnt_inv
Real number (ℝ)

Distinct721
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15159.06243
Minimum1000
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:32.778123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3500
Q18000
median13000
Q320000
95-th percentile35000
Maximum40000
Range39000
Interquartile range (IQR)12000

Descriptive statistics

Standard deviation9290.398471
Coefficient of variation (CV)0.6128610207
Kurtosis-0.1113658307
Mean15159.06243
Median Absolute Deviation (MAD)6337.5
Skewness0.7896955534
Sum75795312.13
Variance86311503.75
MonotonicityNot monotonic
2023-07-21T13:17:32.877273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 393
 
7.9%
20000 257
 
5.1%
15000 248
 
5.0%
12000 238
 
4.8%
5000 197
 
3.9%
35000 177
 
3.5%
8000 167
 
3.3%
30000 145
 
2.9%
6000 143
 
2.9%
16000 135
 
2.7%
Other values (711) 2900
58.0%
ValueCountFrequency (%)
1000 24
0.5%
1075 1
 
< 0.1%
1100 1
 
< 0.1%
1125 1
 
< 0.1%
1200 4
 
0.1%
ValueCountFrequency (%)
40000 84
1.7%
39975 1
 
< 0.1%
39950 1
 
< 0.1%
39750 3
 
0.1%
39725 1
 
< 0.1%

term
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
36 months
3550 
60 months
1450 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters50000
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 36 months
3rd row 36 months
4th row 36 months
5th row 60 months

Common Values

ValueCountFrequency (%)
36 months 3550
71.0%
60 months 1450
29.0%

Length

2023-07-21T13:17:32.969309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:33.048978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
months 5000
50.0%
36 3550
35.5%
60 1450
 
14.5%

Most occurring characters

ValueCountFrequency (%)
10000
20.0%
6 5000
10.0%
m 5000
10.0%
o 5000
10.0%
n 5000
10.0%
t 5000
10.0%
h 5000
10.0%
s 5000
10.0%
3 3550
 
7.1%
0 1450
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 30000
60.0%
Space Separator 10000
 
20.0%
Decimal Number 10000
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 5000
16.7%
o 5000
16.7%
n 5000
16.7%
t 5000
16.7%
h 5000
16.7%
s 5000
16.7%
Decimal Number
ValueCountFrequency (%)
6 5000
50.0%
3 3550
35.5%
0 1450
 
14.5%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 30000
60.0%
Common 20000
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 5000
16.7%
o 5000
16.7%
n 5000
16.7%
t 5000
16.7%
h 5000
16.7%
s 5000
16.7%
Common
ValueCountFrequency (%)
10000
50.0%
6 5000
25.0%
3 3550
 
17.8%
0 1450
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10000
20.0%
6 5000
10.0%
m 5000
10.0%
o 5000
10.0%
n 5000
10.0%
t 5000
10.0%
h 5000
10.0%
s 5000
10.0%
3 3550
 
7.1%
0 1450
 
2.9%

int_rate
Real number (ℝ)

Distinct358
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.172844
Minimum5.31
Maximum30.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:33.123293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5.31
5-th percentile6.46
Q19.49
median12.62
Q316.02
95-th percentile22.451
Maximum30.99
Range25.68
Interquartile range (IQR)6.53

Descriptive statistics

Standard deviation4.945337657
Coefficient of variation (CV)0.3754191318
Kurtosis0.6132394013
Mean13.172844
Median Absolute Deviation (MAD)3.18
Skewness0.7890710036
Sum65864.22
Variance24.45636454
MonotonicityNot monotonic
2023-07-21T13:17:33.224898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.99 114
 
2.3%
5.32 105
 
2.1%
13.99 98
 
2.0%
10.99 93
 
1.9%
11.49 76
 
1.5%
12.99 69
 
1.4%
16.02 67
 
1.3%
15.61 61
 
1.2%
15.05 60
 
1.2%
7.89 60
 
1.2%
Other values (348) 4197
83.9%
ValueCountFrequency (%)
5.31 23
 
0.5%
5.32 105
2.1%
5.42 1
 
< 0.1%
5.79 1
 
< 0.1%
5.93 4
 
0.1%
ValueCountFrequency (%)
30.99 6
0.1%
30.94 1
 
< 0.1%
30.89 1
 
< 0.1%
30.84 2
 
< 0.1%
30.79 3
0.1%

installment
Real number (ℝ)

Distinct3834
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450.160642
Minimum30.46
Maximum1500.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:33.327243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum30.46
5-th percentile117.885
Q1253.2225
median379.34
Q3597.555
95-th percentile993.3435
Maximum1500.22
Range1469.76
Interquartile range (IQR)344.3325

Descriptive statistics

Standard deviation270.9793409
Coefficient of variation (CV)0.6019614235
Kurtosis0.7018568014
Mean450.160642
Median Absolute Deviation (MAD)159.445
Skewness1.013197246
Sum2250803.21
Variance73429.80318
MonotonicityNot monotonic
2023-07-21T13:17:33.423577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
361.38 14
 
0.3%
332.1 10
 
0.2%
410.08 9
 
0.2%
602.3 9
 
0.2%
469.29 9
 
0.2%
329.72 9
 
0.2%
301.15 9
 
0.2%
498.15 9
 
0.2%
339.31 8
 
0.2%
314.48 8
 
0.2%
Other values (3824) 4906
98.1%
ValueCountFrequency (%)
30.46 1
< 0.1%
31.27 1
< 0.1%
31.3 1
< 0.1%
32.01 1
< 0.1%
32.08 1
< 0.1%
ValueCountFrequency (%)
1500.22 1
< 0.1%
1476.05 1
< 0.1%
1466.04 1
< 0.1%
1445.9 1
< 0.1%
1427.91 1
< 0.1%

grade
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
B
1450 
C
1408 
A
964 
D
718 
E
325 
Other values (2)
 
135

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowA
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 1450
29.0%
C 1408
28.2%
A 964
19.3%
D 718
14.4%
E 325
 
6.5%
F 107
 
2.1%
G 28
 
0.6%

Length

2023-07-21T13:17:33.514515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:33.608655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
b 1450
29.0%
c 1408
28.2%
a 964
19.3%
d 718
14.4%
e 325
 
6.5%
f 107
 
2.1%
g 28
 
0.6%

Most occurring characters

ValueCountFrequency (%)
B 1450
29.0%
C 1408
28.2%
A 964
19.3%
D 718
14.4%
E 325
 
6.5%
F 107
 
2.1%
G 28
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 1450
29.0%
C 1408
28.2%
A 964
19.3%
D 718
14.4%
E 325
 
6.5%
F 107
 
2.1%
G 28
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 1450
29.0%
C 1408
28.2%
A 964
19.3%
D 718
14.4%
E 325
 
6.5%
F 107
 
2.1%
G 28
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 1450
29.0%
C 1408
28.2%
A 964
19.3%
D 718
14.4%
E 325
 
6.5%
F 107
 
2.1%
G 28
 
0.6%

sub_grade
Categorical

Distinct35
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
C1
 
324
B5
 
313
B4
 
297
B3
 
294
C2
 
287
Other values (30)
3485 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10000
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB3
2nd rowA1
3rd rowA4
4th rowB5
5th rowB4

Common Values

ValueCountFrequency (%)
C1 324
 
6.5%
B5 313
 
6.3%
B4 297
 
5.9%
B3 294
 
5.9%
C2 287
 
5.7%
C4 284
 
5.7%
B1 273
 
5.5%
B2 273
 
5.5%
C5 264
 
5.3%
C3 249
 
5.0%
Other values (25) 2142
42.8%

Length

2023-07-21T13:17:33.684925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c1 324
 
6.5%
b5 313
 
6.3%
b4 297
 
5.9%
b3 294
 
5.9%
c2 287
 
5.7%
c4 284
 
5.7%
b1 273
 
5.5%
b2 273
 
5.5%
c5 264
 
5.3%
c3 249
 
5.0%
Other values (25) 2142
42.8%

Most occurring characters

ValueCountFrequency (%)
B 1450
14.5%
C 1408
14.1%
1 1072
10.7%
2 1024
10.2%
4 1017
10.2%
5 970
9.7%
A 964
9.6%
3 917
9.2%
D 718
7.2%
E 325
 
3.2%
Other values (2) 135
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5000
50.0%
Decimal Number 5000
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 1450
29.0%
C 1408
28.2%
A 964
19.3%
D 718
14.4%
E 325
 
6.5%
F 107
 
2.1%
G 28
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 1072
21.4%
2 1024
20.5%
4 1017
20.3%
5 970
19.4%
3 917
18.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 5000
50.0%
Common 5000
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 1450
29.0%
C 1408
28.2%
A 964
19.3%
D 718
14.4%
E 325
 
6.5%
F 107
 
2.1%
G 28
 
0.6%
Common
ValueCountFrequency (%)
1 1072
21.4%
2 1024
20.5%
4 1017
20.3%
5 970
19.4%
3 917
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 1450
14.5%
C 1408
14.1%
1 1072
10.7%
2 1024
10.2%
4 1017
10.2%
5 970
9.7%
A 964
9.6%
3 917
9.2%
D 718
7.2%
E 325
 
3.2%
Other values (2) 135
 
1.4%

emp_title
Categorical

HIGH CARDINALITY  MISSING 

Distinct3138
Distinct (%)68.2%
Missing398
Missing (%)8.0%
Memory size78.1 KiB
Manager
 
80
Teacher
 
75
Owner
 
43
Driver
 
43
Registered Nurse
 
40
Other values (3133)
4321 

Length

Max length40
Median length31
Mean length15.4248153
Min length1

Characters and Unicode

Total characters70985
Distinct characters72
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2738 ?
Unique (%)59.5%

Sample

1st rowSurgical Tech
2nd rowPerformance Leader
3rd rowTruck driver
4th rowSHIPPING MANAGUER
5th rowDispatcher

Common Values

ValueCountFrequency (%)
Manager 80
 
1.6%
Teacher 75
 
1.5%
Owner 43
 
0.9%
Driver 43
 
0.9%
Registered Nurse 40
 
0.8%
RN 35
 
0.7%
Supervisor 34
 
0.7%
President 31
 
0.6%
Sales 29
 
0.6%
General Manager 25
 
0.5%
Other values (3128) 4167
83.3%
(Missing) 398
 
8.0%

Length

2023-07-21T13:17:33.782119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
manager 629
 
6.8%
assistant 193
 
2.1%
sales 172
 
1.9%
director 167
 
1.8%
supervisor 147
 
1.6%
driver 133
 
1.4%
engineer 130
 
1.4%
of 122
 
1.3%
specialist 112
 
1.2%
teacher 112
 
1.2%
Other values (2003) 7316
79.2%

Most occurring characters

ValueCountFrequency (%)
e 7749
 
10.9%
r 6223
 
8.8%
a 5458
 
7.7%
5111
 
7.2%
i 4901
 
6.9%
n 4786
 
6.7%
t 4431
 
6.2%
s 3519
 
5.0%
o 3499
 
4.9%
c 2903
 
4.1%
Other values (62) 22405
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55902
78.8%
Uppercase Letter 9608
 
13.5%
Space Separator 5111
 
7.2%
Other Punctuation 268
 
0.4%
Decimal Number 37
 
0.1%
Dash Punctuation 34
 
< 0.1%
Close Punctuation 11
 
< 0.1%
Open Punctuation 11
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7749
13.9%
r 6223
11.1%
a 5458
9.8%
i 4901
8.8%
n 4786
8.6%
t 4431
7.9%
s 3519
 
6.3%
o 3499
 
6.3%
c 2903
 
5.2%
l 2059
 
3.7%
Other values (17) 10374
18.6%
Uppercase Letter
ValueCountFrequency (%)
S 1201
12.5%
A 942
9.8%
M 888
 
9.2%
C 831
 
8.6%
P 720
 
7.5%
T 596
 
6.2%
E 569
 
5.9%
D 547
 
5.7%
R 529
 
5.5%
O 465
 
4.8%
Other values (15) 2320
24.1%
Decimal Number
ValueCountFrequency (%)
2 12
32.4%
1 8
21.6%
6 4
 
10.8%
3 4
 
10.8%
5 3
 
8.1%
0 3
 
8.1%
4 1
 
2.7%
9 1
 
2.7%
7 1
 
2.7%
Other Punctuation
ValueCountFrequency (%)
/ 126
47.0%
. 97
36.2%
& 35
 
13.1%
' 8
 
3.0%
; 1
 
0.4%
\ 1
 
0.4%
Space Separator
ValueCountFrequency (%)
5111
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Math Symbol
ValueCountFrequency (%)
| 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 65510
92.3%
Common 5475
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7749
 
11.8%
r 6223
 
9.5%
a 5458
 
8.3%
i 4901
 
7.5%
n 4786
 
7.3%
t 4431
 
6.8%
s 3519
 
5.4%
o 3499
 
5.3%
c 2903
 
4.4%
l 2059
 
3.1%
Other values (42) 19982
30.5%
Common
ValueCountFrequency (%)
5111
93.4%
/ 126
 
2.3%
. 97
 
1.8%
& 35
 
0.6%
- 34
 
0.6%
2 12
 
0.2%
) 11
 
0.2%
( 11
 
0.2%
' 8
 
0.1%
1 8
 
0.1%
Other values (10) 22
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70984
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7749
 
10.9%
r 6223
 
8.8%
a 5458
 
7.7%
5111
 
7.2%
i 4901
 
6.9%
n 4786
 
6.7%
t 4431
 
6.2%
s 3519
 
5.0%
o 3499
 
4.9%
c 2903
 
4.1%
Other values (61) 22404
31.6%
None
ValueCountFrequency (%)
ó 1
100.0%

emp_length
Categorical

Distinct11
Distinct (%)0.2%
Missing346
Missing (%)6.9%
Memory size78.1 KiB
10+ years
1683 
2 years
453 
< 1 year
418 
3 years
390 
1 year
311 
Other values (6)
1399 

Length

Max length9
Median length8
Mean length7.746239794
Min length6

Characters and Unicode

Total characters36051
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10+ years
2nd row7 years
3rd row6 years
4th row7 years
5th row7 years

Common Values

ValueCountFrequency (%)
10+ years 1683
33.7%
2 years 453
 
9.1%
< 1 year 418
 
8.4%
3 years 390
 
7.8%
1 year 311
 
6.2%
4 years 296
 
5.9%
5 years 287
 
5.7%
6 years 231
 
4.6%
8 years 207
 
4.1%
9 years 192
 
3.8%
(Missing) 346
 
6.9%

Length

2023-07-21T13:17:33.878464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 3925
40.4%
10 1683
17.3%
1 729
 
7.5%
year 729
 
7.5%
2 453
 
4.7%
418
 
4.3%
3 390
 
4.0%
4 296
 
3.0%
5 287
 
3.0%
6 231
 
2.4%
Other values (3) 585
 
6.0%

Most occurring characters

ValueCountFrequency (%)
5072
14.1%
y 4654
12.9%
e 4654
12.9%
a 4654
12.9%
r 4654
12.9%
s 3925
10.9%
1 2412
6.7%
0 1683
 
4.7%
+ 1683
 
4.7%
2 453
 
1.3%
Other values (8) 2207
6.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22541
62.5%
Decimal Number 6337
 
17.6%
Space Separator 5072
 
14.1%
Math Symbol 2101
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2412
38.1%
0 1683
26.6%
2 453
 
7.1%
3 390
 
6.2%
4 296
 
4.7%
5 287
 
4.5%
6 231
 
3.6%
8 207
 
3.3%
9 192
 
3.0%
7 186
 
2.9%
Lowercase Letter
ValueCountFrequency (%)
y 4654
20.6%
e 4654
20.6%
a 4654
20.6%
r 4654
20.6%
s 3925
17.4%
Math Symbol
ValueCountFrequency (%)
+ 1683
80.1%
< 418
 
19.9%
Space Separator
ValueCountFrequency (%)
5072
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22541
62.5%
Common 13510
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
5072
37.5%
1 2412
17.9%
0 1683
 
12.5%
+ 1683
 
12.5%
2 453
 
3.4%
< 418
 
3.1%
3 390
 
2.9%
4 296
 
2.2%
5 287
 
2.1%
6 231
 
1.7%
Other values (3) 585
 
4.3%
Latin
ValueCountFrequency (%)
y 4654
20.6%
e 4654
20.6%
a 4654
20.6%
r 4654
20.6%
s 3925
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5072
14.1%
y 4654
12.9%
e 4654
12.9%
a 4654
12.9%
r 4654
12.9%
s 3925
10.9%
1 2412
6.7%
0 1683
 
4.7%
+ 1683
 
4.7%
2 453
 
1.3%
Other values (8) 2207
6.1%

home_ownership
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
MORTGAGE
2489 
RENT
1983 
OWN
526 
ANY
 
2

Length

Max length8
Median length4
Mean length5.8856
Min length3

Characters and Unicode

Total characters29428
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMORTGAGE
2nd rowMORTGAGE
3rd rowMORTGAGE
4th rowMORTGAGE
5th rowRENT

Common Values

ValueCountFrequency (%)
MORTGAGE 2489
49.8%
RENT 1983
39.7%
OWN 526
 
10.5%
ANY 2
 
< 0.1%

Length

2023-07-21T13:17:34.296509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:34.377321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
mortgage 2489
49.8%
rent 1983
39.7%
own 526
 
10.5%
any 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
G 4978
16.9%
R 4472
15.2%
T 4472
15.2%
E 4472
15.2%
O 3015
10.2%
N 2511
8.5%
A 2491
8.5%
M 2489
8.5%
W 526
 
1.8%
Y 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 29428
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 4978
16.9%
R 4472
15.2%
T 4472
15.2%
E 4472
15.2%
O 3015
10.2%
N 2511
8.5%
A 2491
8.5%
M 2489
8.5%
W 526
 
1.8%
Y 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 29428
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 4978
16.9%
R 4472
15.2%
T 4472
15.2%
E 4472
15.2%
O 3015
10.2%
N 2511
8.5%
A 2491
8.5%
M 2489
8.5%
W 526
 
1.8%
Y 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 4978
16.9%
R 4472
15.2%
T 4472
15.2%
E 4472
15.2%
O 3015
10.2%
N 2511
8.5%
A 2491
8.5%
M 2489
8.5%
W 526
 
1.8%
Y 2
 
< 0.1%

annual_inc
Real number (ℝ)

Distinct857
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78248.21034
Minimum0
Maximum1750000
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:34.459797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28000
Q145829.25
median65000
Q395000
95-th percentile165000
Maximum1750000
Range1750000
Interquartile range (IQR)49170.75

Descriptive statistics

Standard deviation57724.64402
Coefficient of variation (CV)0.7377120034
Kurtosis162.5345851
Mean78248.21034
Median Absolute Deviation (MAD)23000
Skewness7.819938479
Sum391241051.7
Variance3332134527
MonotonicityNot monotonic
2023-07-21T13:17:34.557570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 165
 
3.3%
50000 153
 
3.1%
65000 150
 
3.0%
40000 136
 
2.7%
70000 134
 
2.7%
45000 130
 
2.6%
75000 125
 
2.5%
80000 124
 
2.5%
100000 111
 
2.2%
55000 107
 
2.1%
Other values (847) 3665
73.3%
ValueCountFrequency (%)
0 3
0.1%
500 1
 
< 0.1%
2500 1
 
< 0.1%
5000 1
 
< 0.1%
8000 1
 
< 0.1%
ValueCountFrequency (%)
1750000 1
< 0.1%
830000 1
< 0.1%
800000 1
< 0.1%
750000 1
< 0.1%
700000 1
< 0.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Source Verified
1965 
Not Verified
1648 
Verified
1387 

Length

Max length15
Median length12
Mean length12.0694
Min length8

Characters and Unicode

Total characters60347
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVerified
2nd rowNot Verified
3rd rowNot Verified
4th rowSource Verified
5th rowSource Verified

Common Values

ValueCountFrequency (%)
Source Verified 1965
39.3%
Not Verified 1648
33.0%
Verified 1387
27.7%

Length

2023-07-21T13:17:34.653165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:34.735943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
verified 5000
58.1%
source 1965
 
22.8%
not 1648
 
19.1%

Most occurring characters

ValueCountFrequency (%)
e 11965
19.8%
i 10000
16.6%
r 6965
11.5%
V 5000
8.3%
f 5000
8.3%
d 5000
8.3%
o 3613
 
6.0%
3613
 
6.0%
S 1965
 
3.3%
u 1965
 
3.3%
Other values (3) 5261
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48121
79.7%
Uppercase Letter 8613
 
14.3%
Space Separator 3613
 
6.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11965
24.9%
i 10000
20.8%
r 6965
14.5%
f 5000
10.4%
d 5000
10.4%
o 3613
 
7.5%
u 1965
 
4.1%
c 1965
 
4.1%
t 1648
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
V 5000
58.1%
S 1965
 
22.8%
N 1648
 
19.1%
Space Separator
ValueCountFrequency (%)
3613
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56734
94.0%
Common 3613
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11965
21.1%
i 10000
17.6%
r 6965
12.3%
V 5000
8.8%
f 5000
8.8%
d 5000
8.8%
o 3613
 
6.4%
S 1965
 
3.5%
u 1965
 
3.5%
c 1965
 
3.5%
Other values (2) 3296
 
5.8%
Common
ValueCountFrequency (%)
3613
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11965
19.8%
i 10000
16.6%
r 6965
11.5%
V 5000
8.3%
f 5000
8.3%
d 5000
8.3%
o 3613
 
6.0%
3613
 
6.0%
S 1965
 
3.3%
u 1965
 
3.3%
Other values (3) 5261
8.7%

issue_d
Categorical

Distinct115
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Mar-2016
 
133
Nov-2017
 
118
Jul-2015
 
115
May-2018
 
112
Oct-2018
 
110
Other values (110)
4412 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters40000
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.3%

Sample

1st rowDec-2017
2nd rowAug-2015
3rd rowDec-2017
4th rowDec-2015
5th rowOct-2014

Common Values

ValueCountFrequency (%)
Mar-2016 133
 
2.7%
Nov-2017 118
 
2.4%
Jul-2015 115
 
2.3%
May-2018 112
 
2.2%
Oct-2018 110
 
2.2%
Aug-2018 110
 
2.2%
Nov-2018 108
 
2.2%
Apr-2018 108
 
2.2%
Sep-2017 102
 
2.0%
Oct-2014 100
 
2.0%
Other values (105) 3884
77.7%

Length

2023-07-21T13:17:34.804119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mar-2016 133
 
2.7%
nov-2017 118
 
2.4%
jul-2015 115
 
2.3%
may-2018 112
 
2.2%
oct-2018 110
 
2.2%
aug-2018 110
 
2.2%
nov-2018 108
 
2.2%
apr-2018 108
 
2.2%
sep-2017 102
 
2.0%
oct-2014 100
 
2.0%
Other values (105) 3884
77.7%

Most occurring characters

ValueCountFrequency (%)
2 5089
 
12.7%
1 5027
 
12.6%
0 5025
 
12.6%
- 5000
 
12.5%
u 1321
 
3.3%
J 1211
 
3.0%
a 1199
 
3.0%
8 1140
 
2.9%
e 1084
 
2.7%
7 1018
 
2.5%
Other values (23) 12886
32.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
50.0%
Lowercase Letter 10000
25.0%
Dash Punctuation 5000
 
12.5%
Uppercase Letter 5000
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 1321
13.2%
a 1199
12.0%
e 1084
10.8%
c 875
8.8%
r 849
8.5%
p 804
8.0%
n 736
7.4%
t 509
 
5.1%
v 479
 
4.8%
o 479
 
4.8%
Other values (4) 1665
16.7%
Decimal Number
ValueCountFrequency (%)
2 5089
25.4%
1 5027
25.1%
0 5025
25.1%
8 1140
 
5.7%
7 1018
 
5.1%
5 953
 
4.8%
6 928
 
4.6%
4 557
 
2.8%
3 254
 
1.3%
9 9
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
J 1211
24.2%
A 865
17.3%
M 852
17.0%
O 509
10.2%
N 479
 
9.6%
S 396
 
7.9%
D 366
 
7.3%
F 322
 
6.4%
Dash Punctuation
ValueCountFrequency (%)
- 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25000
62.5%
Latin 15000
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 1321
 
8.8%
J 1211
 
8.1%
a 1199
 
8.0%
e 1084
 
7.2%
c 875
 
5.8%
A 865
 
5.8%
M 852
 
5.7%
r 849
 
5.7%
p 804
 
5.4%
n 736
 
4.9%
Other values (12) 5204
34.7%
Common
ValueCountFrequency (%)
2 5089
20.4%
1 5027
20.1%
0 5025
20.1%
- 5000
20.0%
8 1140
 
4.6%
7 1018
 
4.1%
5 953
 
3.8%
6 928
 
3.7%
4 557
 
2.2%
3 254
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5089
 
12.7%
1 5027
 
12.6%
0 5025
 
12.6%
- 5000
 
12.5%
u 1321
 
3.3%
J 1211
 
3.0%
a 1199
 
3.0%
8 1140
 
2.9%
e 1084
 
2.7%
7 1018
 
2.5%
Other values (23) 12886
32.2%

loan_status
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Fully Paid
2225 
Current
2055 
Charged Off
638 
Late (31-120 days)
 
61
In Grace Period
 
11
Other values (2)
 
10

Length

Max length50
Median length18
Mean length9.0238
Min length7

Characters and Unicode

Total characters45119
Distinct characters38
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCurrent
2nd rowFully Paid
3rd rowCurrent
4th rowFully Paid
5th rowFully Paid

Common Values

ValueCountFrequency (%)
Fully Paid 2225
44.5%
Current 2055
41.1%
Charged Off 638
 
12.8%
Late (31-120 days) 61
 
1.2%
In Grace Period 11
 
0.2%
Late (16-30 days) 9
 
0.2%
Does not meet the credit policy. Status:Fully Paid 1
 
< 0.1%

Length

2023-07-21T13:17:34.878897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:34.967323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
paid 2226
27.7%
fully 2225
27.7%
current 2055
25.6%
charged 638
 
7.9%
off 638
 
7.9%
late 70
 
0.9%
days 70
 
0.9%
31-120 61
 
0.8%
period 11
 
0.1%
grace 11
 
0.1%
Other values (9) 27
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r 4771
 
10.6%
l 4453
 
9.9%
u 4282
 
9.5%
3032
 
6.7%
a 3016
 
6.7%
d 2946
 
6.5%
e 2790
 
6.2%
C 2693
 
6.0%
y 2297
 
5.1%
i 2239
 
5.0%
Other values (28) 12600
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33646
74.6%
Uppercase Letter 7888
 
17.5%
Space Separator 3032
 
6.7%
Decimal Number 341
 
0.8%
Close Punctuation 70
 
0.2%
Open Punctuation 70
 
0.2%
Dash Punctuation 70
 
0.2%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 4771
14.2%
l 4453
13.2%
u 4282
12.7%
a 3016
9.0%
d 2946
8.8%
e 2790
8.3%
y 2297
6.8%
i 2239
6.7%
t 2131
6.3%
n 2067
6.1%
Other values (8) 2654
7.9%
Uppercase Letter
ValueCountFrequency (%)
C 2693
34.1%
P 2237
28.4%
F 2226
28.2%
O 638
 
8.1%
L 70
 
0.9%
I 11
 
0.1%
G 11
 
0.1%
D 1
 
< 0.1%
S 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 131
38.4%
0 70
20.5%
3 70
20.5%
2 61
17.9%
6 9
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
: 1
50.0%
Space Separator
ValueCountFrequency (%)
3032
100.0%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%
Open Punctuation
ValueCountFrequency (%)
( 70
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41534
92.1%
Common 3585
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 4771
11.5%
l 4453
10.7%
u 4282
10.3%
a 3016
 
7.3%
d 2946
 
7.1%
e 2790
 
6.7%
C 2693
 
6.5%
y 2297
 
5.5%
i 2239
 
5.4%
P 2237
 
5.4%
Other values (17) 9810
23.6%
Common
ValueCountFrequency (%)
3032
84.6%
1 131
 
3.7%
) 70
 
2.0%
0 70
 
2.0%
( 70
 
2.0%
- 70
 
2.0%
3 70
 
2.0%
2 61
 
1.7%
6 9
 
0.3%
. 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 4771
 
10.6%
l 4453
 
9.9%
u 4282
 
9.5%
3032
 
6.7%
a 3016
 
6.7%
d 2946
 
6.5%
e 2790
 
6.2%
C 2693
 
6.0%
y 2297
 
5.1%
i 2239
 
5.0%
Other values (28) 12600
27.9%

pymnt_plan
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
False
4997 
True
 
3
ValueCountFrequency (%)
False 4997
99.9%
True 3
 
0.1%
2023-07-21T13:17:35.052378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

url
Categorical

HIGH CARDINALITY  UNIQUE 

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
https://lendingclub.com/browse/loanDetail.action?loan_id=125102239
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=36930300
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=113511435
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=126269972
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=129913666
 
1
Other values (4995)
4995 

Length

Max length66
Median length65
Mean length65.3036
Min length63

Characters and Unicode

Total characters326518
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5000 ?
Unique (%)100.0%

Sample

1st rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=125102239
2nd rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=58350350
3rd rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=125886282
4th rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=67477661
5th rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=27240428

Common Values

ValueCountFrequency (%)
https://lendingclub.com/browse/loanDetail.action?loan_id=125102239 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=36930300 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=113511435 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=126269972 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=129913666 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=140641530 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=93563098 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=67457043 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=142271868 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=85610857 1
 
< 0.1%
Other values (4990) 4990
99.8%

Length

2023-07-21T13:17:35.118944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://lendingclub.com/browse/loandetail.action?loan_id=125102239 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=53494561 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=27240428 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1521045 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=97944934 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=119274480 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=49297432 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=48996708 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=110010430 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=40268147 1
 
< 0.1%
Other values (4990) 4990
99.8%

Most occurring characters

ValueCountFrequency (%)
l 25000
 
7.7%
n 25000
 
7.7%
o 25000
 
7.7%
t 20000
 
6.1%
/ 20000
 
6.1%
a 20000
 
6.1%
i 20000
 
6.1%
e 15000
 
4.6%
c 15000
 
4.6%
d 10000
 
3.1%
Other values (25) 131518
40.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 230000
70.4%
Decimal Number 41518
 
12.7%
Other Punctuation 40000
 
12.3%
Connector Punctuation 5000
 
1.5%
Math Symbol 5000
 
1.5%
Uppercase Letter 5000
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 25000
10.9%
n 25000
10.9%
o 25000
10.9%
t 20000
8.7%
a 20000
8.7%
i 20000
8.7%
e 15000
 
6.5%
c 15000
 
6.5%
d 10000
 
4.3%
b 10000
 
4.3%
Other values (8) 45000
19.6%
Decimal Number
ValueCountFrequency (%)
1 6206
14.9%
3 4292
10.3%
4 4204
10.1%
2 4142
10.0%
6 3921
9.4%
5 3865
9.3%
7 3836
9.2%
9 3745
9.0%
0 3719
9.0%
8 3588
8.6%
Other Punctuation
ValueCountFrequency (%)
/ 20000
50.0%
. 10000
25.0%
? 5000
 
12.5%
: 5000
 
12.5%
Connector Punctuation
ValueCountFrequency (%)
_ 5000
100.0%
Math Symbol
ValueCountFrequency (%)
= 5000
100.0%
Uppercase Letter
ValueCountFrequency (%)
D 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 235000
72.0%
Common 91518
 
28.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 25000
10.6%
n 25000
10.6%
o 25000
10.6%
t 20000
 
8.5%
a 20000
 
8.5%
i 20000
 
8.5%
e 15000
 
6.4%
c 15000
 
6.4%
d 10000
 
4.3%
b 10000
 
4.3%
Other values (9) 50000
21.3%
Common
ValueCountFrequency (%)
/ 20000
21.9%
. 10000
10.9%
1 6206
 
6.8%
? 5000
 
5.5%
_ 5000
 
5.5%
= 5000
 
5.5%
: 5000
 
5.5%
3 4292
 
4.7%
4 4204
 
4.6%
2 4142
 
4.5%
Other values (6) 22674
24.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 326518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 25000
 
7.7%
n 25000
 
7.7%
o 25000
 
7.7%
t 20000
 
6.1%
/ 20000
 
6.1%
a 20000
 
6.1%
i 20000
 
6.1%
e 15000
 
4.6%
c 15000
 
4.6%
d 10000
 
3.1%
Other values (25) 131518
40.3%

purpose
Categorical

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
debt_consolidation
2824 
credit_card
1151 
other
315 
home_improvement
300 
major_purchase
 
130
Other values (9)
 
280

Length

Max length18
Median length18
Mean length14.759
Min length3

Characters and Unicode

Total characters73795
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowmedical
2nd rowdebt_consolidation
3rd rowcredit_card
4th rowhome_improvement
5th rowcredit_card

Common Values

ValueCountFrequency (%)
debt_consolidation 2824
56.5%
credit_card 1151
23.0%
other 315
 
6.3%
home_improvement 300
 
6.0%
major_purchase 130
 
2.6%
medical 85
 
1.7%
small_business 49
 
1.0%
car 47
 
0.9%
vacation 34
 
0.7%
moving 30
 
0.6%
Other values (4) 35
 
0.7%

Length

2023-07-21T13:17:35.206977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation 2824
56.5%
credit_card 1151
23.0%
other 315
 
6.3%
home_improvement 300
 
6.0%
major_purchase 130
 
2.6%
medical 85
 
1.7%
small_business 49
 
1.0%
car 47
 
0.9%
vacation 34
 
0.7%
moving 30
 
0.6%
Other values (4) 35
 
0.7%

Most occurring characters

ValueCountFrequency (%)
o 9608
13.0%
d 8044
10.9%
t 7449
10.1%
i 7302
9.9%
n 6074
8.2%
e 5505
7.5%
c 5423
7.3%
a 4490
 
6.1%
_ 4458
 
6.0%
r 3232
 
4.4%
Other values (12) 12210
16.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 69337
94.0%
Connector Punctuation 4458
 
6.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 9608
13.9%
d 8044
11.6%
t 7449
10.7%
i 7302
10.5%
n 6074
8.8%
e 5505
7.9%
c 5423
7.8%
a 4490
6.5%
r 3232
 
4.7%
s 3176
 
4.6%
Other values (11) 9034
13.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4458
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 69337
94.0%
Common 4458
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 9608
13.9%
d 8044
11.6%
t 7449
10.7%
i 7302
10.5%
n 6074
8.8%
e 5505
7.9%
c 5423
7.8%
a 4490
6.5%
r 3232
 
4.7%
s 3176
 
4.6%
Other values (11) 9034
13.0%
Common
ValueCountFrequency (%)
_ 4458
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 9608
13.0%
d 8044
10.9%
t 7449
10.1%
i 7302
9.9%
n 6074
8.2%
e 5505
7.5%
c 5423
7.3%
a 4490
 
6.1%
_ 4458
 
6.0%
r 3232
 
4.4%
Other values (12) 12210
16.5%

title
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct264
Distinct (%)5.3%
Missing61
Missing (%)1.2%
Memory size78.1 KiB
Debt consolidation
2595 
Credit card refinancing
1060 
Other
285 
Home improvement
279 
Major purchase
 
116
Other values (259)
604 

Length

Max length40
Median length18
Mean length17.69791456
Min length2

Characters and Unicode

Total characters87410
Distinct characters64
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique222 ?
Unique (%)4.5%

Sample

1st rowMedical expenses
2nd rowDebt consolidation
3rd rowCredit card refinancing
4th rowHome improvement
5th rowCredit card refinancing

Common Values

ValueCountFrequency (%)
Debt consolidation 2595
51.9%
Credit card refinancing 1060
21.2%
Other 285
 
5.7%
Home improvement 279
 
5.6%
Major purchase 116
 
2.3%
Medical expenses 80
 
1.6%
Business 45
 
0.9%
Car financing 42
 
0.8%
Vacation 34
 
0.7%
Debt Consolidation 31
 
0.6%
Other values (254) 372
 
7.4%
(Missing) 61
 
1.2%

Length

2023-07-21T13:17:35.309311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt 2688
25.1%
consolidation 2677
25.0%
credit 1111
10.4%
card 1101
10.3%
refinancing 1064
 
9.9%
home 315
 
2.9%
improvement 287
 
2.7%
other 286
 
2.7%
purchase 121
 
1.1%
major 118
 
1.1%
Other values (231) 955
 
8.9%

Most occurring characters

ValueCountFrequency (%)
n 9497
10.9%
i 9361
10.7%
o 9139
10.5%
t 7249
 
8.3%
e 6824
 
7.8%
5802
 
6.6%
a 5623
 
6.4%
c 5142
 
5.9%
d 5104
 
5.8%
r 4304
 
4.9%
Other values (54) 19365
22.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76312
87.3%
Space Separator 5802
 
6.6%
Uppercase Letter 5255
 
6.0%
Other Punctuation 21
 
< 0.1%
Decimal Number 14
 
< 0.1%
Dash Punctuation 4
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 9497
12.4%
i 9361
12.3%
o 9139
12.0%
t 7249
9.5%
e 6824
8.9%
a 5623
7.4%
c 5142
6.7%
d 5104
6.7%
r 4304
 
5.6%
s 3208
 
4.2%
Other values (16) 10861
14.2%
Uppercase Letter
ValueCountFrequency (%)
D 2683
51.1%
C 1279
24.3%
H 318
 
6.1%
O 315
 
6.0%
M 240
 
4.6%
L 73
 
1.4%
B 58
 
1.1%
P 45
 
0.9%
V 38
 
0.7%
R 33
 
0.6%
Other values (12) 173
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 7
33.3%
& 5
23.8%
/ 4
19.0%
: 2
 
9.5%
! 1
 
4.8%
% 1
 
4.8%
' 1
 
4.8%
Decimal Number
ValueCountFrequency (%)
1 5
35.7%
2 3
21.4%
0 2
 
14.3%
8 2
 
14.3%
4 1
 
7.1%
3 1
 
7.1%
Space Separator
ValueCountFrequency (%)
5802
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81567
93.3%
Common 5843
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 9497
11.6%
i 9361
11.5%
o 9139
11.2%
t 7249
8.9%
e 6824
8.4%
a 5623
 
6.9%
c 5142
 
6.3%
d 5104
 
6.3%
r 4304
 
5.3%
s 3208
 
3.9%
Other values (38) 16116
19.8%
Common
ValueCountFrequency (%)
5802
99.3%
. 7
 
0.1%
1 5
 
0.1%
& 5
 
0.1%
/ 4
 
0.1%
- 4
 
0.1%
2 3
 
0.1%
: 2
 
< 0.1%
) 2
 
< 0.1%
0 2
 
< 0.1%
Other values (6) 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 9497
10.9%
i 9361
10.7%
o 9139
10.5%
t 7249
 
8.3%
e 6824
 
7.8%
5802
 
6.6%
a 5623
 
6.4%
c 5142
 
5.9%
d 5104
 
5.8%
r 4304
 
4.9%
Other values (54) 19365
22.2%

zip_code
Categorical

Distinct711
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
945xx
 
57
112xx
 
54
606xx
 
50
750xx
 
47
300xx
 
46
Other values (706)
4746 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters25000
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique133 ?
Unique (%)2.7%

Sample

1st row705xx
2nd row301xx
3rd row553xx
4th row070xx
5th row159xx

Common Values

ValueCountFrequency (%)
945xx 57
 
1.1%
112xx 54
 
1.1%
606xx 50
 
1.0%
750xx 47
 
0.9%
300xx 46
 
0.9%
891xx 44
 
0.9%
917xx 42
 
0.8%
770xx 41
 
0.8%
330xx 40
 
0.8%
100xx 37
 
0.7%
Other values (701) 4542
90.8%

Length

2023-07-21T13:17:35.397991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
945xx 57
 
1.1%
112xx 54
 
1.1%
606xx 50
 
1.0%
750xx 47
 
0.9%
300xx 46
 
0.9%
891xx 44
 
0.9%
917xx 42
 
0.8%
770xx 41
 
0.8%
330xx 40
 
0.8%
070xx 37
 
0.7%
Other values (701) 4542
90.8%

Most occurring characters

ValueCountFrequency (%)
x 10000
40.0%
0 2194
 
8.8%
1 1786
 
7.1%
3 1678
 
6.7%
2 1526
 
6.1%
9 1525
 
6.1%
7 1513
 
6.1%
4 1273
 
5.1%
8 1244
 
5.0%
5 1177
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15000
60.0%
Lowercase Letter 10000
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2194
14.6%
1 1786
11.9%
3 1678
11.2%
2 1526
10.2%
9 1525
10.2%
7 1513
10.1%
4 1273
8.5%
8 1244
8.3%
5 1177
7.8%
6 1084
7.2%
Lowercase Letter
ValueCountFrequency (%)
x 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15000
60.0%
Latin 10000
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2194
14.6%
1 1786
11.9%
3 1678
11.2%
2 1526
10.2%
9 1525
10.2%
7 1513
10.1%
4 1273
8.5%
8 1244
8.3%
5 1177
7.8%
6 1084
7.2%
Latin
ValueCountFrequency (%)
x 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
x 10000
40.0%
0 2194
 
8.8%
1 1786
 
7.1%
3 1678
 
6.7%
2 1526
 
6.1%
9 1525
 
6.1%
7 1513
 
6.1%
4 1273
 
5.1%
8 1244
 
5.0%
5 1177
 
4.7%

addr_state
Categorical

Distinct50
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
CA
733 
NY
418 
TX
397 
FL
342 
IL
 
204
Other values (45)
2906 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10000
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLA
2nd rowGA
3rd rowMN
4th rowNJ
5th rowPA

Common Values

ValueCountFrequency (%)
CA 733
 
14.7%
NY 418
 
8.4%
TX 397
 
7.9%
FL 342
 
6.8%
IL 204
 
4.1%
NJ 184
 
3.7%
GA 168
 
3.4%
PA 162
 
3.2%
OH 162
 
3.2%
VA 148
 
3.0%
Other values (40) 2082
41.6%

Length

2023-07-21T13:17:35.468415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 733
 
14.7%
ny 418
 
8.4%
tx 397
 
7.9%
fl 342
 
6.8%
il 204
 
4.1%
nj 184
 
3.7%
ga 168
 
3.4%
pa 162
 
3.2%
oh 162
 
3.2%
va 148
 
3.0%
Other values (40) 2082
41.6%

Most occurring characters

ValueCountFrequency (%)
A 1699
17.0%
N 1172
11.7%
C 1111
11.1%
L 641
 
6.4%
T 612
 
6.1%
M 606
 
6.1%
I 534
 
5.3%
Y 489
 
4.9%
O 439
 
4.4%
X 397
 
4.0%
Other values (14) 2300
23.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1699
17.0%
N 1172
11.7%
C 1111
11.1%
L 641
 
6.4%
T 612
 
6.1%
M 606
 
6.1%
I 534
 
5.3%
Y 489
 
4.9%
O 439
 
4.4%
X 397
 
4.0%
Other values (14) 2300
23.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1699
17.0%
N 1172
11.7%
C 1111
11.1%
L 641
 
6.4%
T 612
 
6.1%
M 606
 
6.1%
I 534
 
5.3%
Y 489
 
4.9%
O 439
 
4.4%
X 397
 
4.0%
Other values (14) 2300
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1699
17.0%
N 1172
11.7%
C 1111
11.1%
L 641
 
6.4%
T 612
 
6.1%
M 606
 
6.1%
I 534
 
5.3%
Y 489
 
4.9%
O 439
 
4.4%
X 397
 
4.0%
Other values (14) 2300
23.0%

dti
Real number (ℝ)

Distinct2607
Distinct (%)52.2%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean19.06747248
Minimum0
Maximum565.85
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:35.554747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.966
Q112.11
median17.92
Q324.77
95-th percentile33.966
Maximum565.85
Range565.85
Interquartile range (IQR)12.66

Descriptive statistics

Standard deviation15.11699037
Coefficient of variation (CV)0.7928156383
Kurtosis589.8467956
Mean19.06747248
Median Absolute Deviation (MAD)6.26
Skewness18.75374497
Sum95280.16
Variance228.5233978
MonotonicityNot monotonic
2023-07-21T13:17:35.643058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.25 10
 
0.2%
18.24 9
 
0.2%
13.47 8
 
0.2%
15.17 7
 
0.1%
14.47 7
 
0.1%
15.62 7
 
0.1%
15.16 7
 
0.1%
11.46 7
 
0.1%
14.81 7
 
0.1%
21.04 7
 
0.1%
Other values (2597) 4921
98.4%
ValueCountFrequency (%)
0 5
0.1%
0.28 1
 
< 0.1%
0.36 1
 
< 0.1%
0.43 1
 
< 0.1%
0.46 1
 
< 0.1%
ValueCountFrequency (%)
565.85 1
< 0.1%
466.92 1
< 0.1%
412.07 1
< 0.1%
157.63 1
< 0.1%
131 1
< 0.1%

delinq_2yrs
Real number (ℝ)

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3134
Minimum0
Maximum13
Zeros4067
Zeros (%)81.3%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:35.721175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8780288303
Coefficient of variation (CV)2.801623581
Kurtosis38.57829573
Mean0.3134
Median Absolute Deviation (MAD)0
Skewness5.035250006
Sum1567
Variance0.7709346269
MonotonicityNot monotonic
2023-07-21T13:17:35.795359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 4067
81.3%
1 617
 
12.3%
2 178
 
3.6%
3 62
 
1.2%
4 34
 
0.7%
5 16
 
0.3%
7 11
 
0.2%
6 10
 
0.2%
11 1
 
< 0.1%
9 1
 
< 0.1%
Other values (3) 3
 
0.1%
ValueCountFrequency (%)
0 4067
81.3%
1 617
 
12.3%
2 178
 
3.6%
3 62
 
1.2%
4 34
 
0.7%
ValueCountFrequency (%)
13 1
< 0.1%
12 1
< 0.1%
11 1
< 0.1%
10 1
< 0.1%
9 1
< 0.1%

earliest_cr_line
Categorical

Distinct489
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Sep-2002
 
39
Nov-2005
 
36
Aug-2003
 
35
Oct-2003
 
35
Jun-2005
 
35
Other values (484)
4820 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters40000
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)1.4%

Sample

1st rowJan-2005
2nd rowApr-2004
3rd rowAug-2001
4th rowJul-1996
5th rowOct-1992

Common Values

ValueCountFrequency (%)
Sep-2002 39
 
0.8%
Nov-2005 36
 
0.7%
Aug-2003 35
 
0.7%
Oct-2003 35
 
0.7%
Jun-2005 35
 
0.7%
Sep-2005 34
 
0.7%
Aug-2002 33
 
0.7%
Mar-2004 32
 
0.6%
Sep-2003 32
 
0.6%
Aug-2005 32
 
0.6%
Other values (479) 4657
93.1%

Length

2023-07-21T13:17:35.878019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sep-2002 39
 
0.8%
nov-2005 36
 
0.7%
aug-2003 35
 
0.7%
oct-2003 35
 
0.7%
jun-2005 35
 
0.7%
sep-2005 34
 
0.7%
aug-2002 33
 
0.7%
jul-2004 32
 
0.6%
aug-2001 32
 
0.6%
aug-2005 32
 
0.6%
Other values (479) 4657
93.1%

Most occurring characters

ValueCountFrequency (%)
0 5973
14.9%
- 5000
 
12.5%
9 4025
 
10.1%
2 3413
 
8.5%
1 2911
 
7.3%
u 1274
 
3.2%
e 1260
 
3.1%
a 1169
 
2.9%
J 1149
 
2.9%
8 908
 
2.3%
Other values (23) 12918
32.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
50.0%
Lowercase Letter 10000
25.0%
Dash Punctuation 5000
 
12.5%
Uppercase Letter 5000
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 1274
12.7%
e 1260
12.6%
a 1169
11.7%
p 884
8.8%
c 875
8.8%
r 798
8.0%
n 745
7.4%
g 481
 
4.8%
t 468
 
4.7%
v 452
 
4.5%
Other values (4) 1594
15.9%
Decimal Number
ValueCountFrequency (%)
0 5973
29.9%
9 4025
20.1%
2 3413
17.1%
1 2911
14.6%
8 908
 
4.5%
5 588
 
2.9%
4 559
 
2.8%
7 554
 
2.8%
3 552
 
2.8%
6 517
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
J 1149
23.0%
A 858
17.2%
M 813
16.3%
S 507
10.1%
O 468
9.4%
N 452
 
9.0%
D 407
 
8.1%
F 346
 
6.9%
Dash Punctuation
ValueCountFrequency (%)
- 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25000
62.5%
Latin 15000
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 1274
 
8.5%
e 1260
 
8.4%
a 1169
 
7.8%
J 1149
 
7.7%
p 884
 
5.9%
c 875
 
5.8%
A 858
 
5.7%
M 813
 
5.4%
r 798
 
5.3%
n 745
 
5.0%
Other values (12) 5175
34.5%
Common
ValueCountFrequency (%)
0 5973
23.9%
- 5000
20.0%
9 4025
16.1%
2 3413
13.7%
1 2911
11.6%
8 908
 
3.6%
5 588
 
2.4%
4 559
 
2.2%
7 554
 
2.2%
3 552
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5973
14.9%
- 5000
 
12.5%
9 4025
 
10.1%
2 3413
 
8.5%
1 2911
 
7.3%
u 1274
 
3.2%
e 1260
 
3.1%
a 1169
 
2.9%
J 1149
 
2.9%
8 908
 
2.3%
Other values (23) 12918
32.3%

fico_range_low
Real number (ℝ)

Distinct38
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean698.845
Minimum660
Maximum845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:35.959204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum660
5-th percentile660
Q1675
median690
Q3715
95-th percentile765
Maximum845
Range185
Interquartile range (IQR)40

Descriptive statistics

Standard deviation33.23780867
Coefficient of variation (CV)0.04756105957
Kurtosis1.410382979
Mean698.845
Median Absolute Deviation (MAD)20
Skewness1.201890058
Sum3494225
Variance1104.751925
MonotonicityNot monotonic
2023-07-21T13:17:36.046391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
660 433
 
8.7%
670 403
 
8.1%
665 392
 
7.8%
680 364
 
7.3%
675 334
 
6.7%
685 316
 
6.3%
690 316
 
6.3%
700 290
 
5.8%
695 289
 
5.8%
705 247
 
4.9%
Other values (28) 1616
32.3%
ValueCountFrequency (%)
660 433
8.7%
665 392
7.8%
670 403
8.1%
675 334
6.7%
680 364
7.3%
ValueCountFrequency (%)
845 1
 
< 0.1%
840 3
0.1%
835 3
0.1%
830 3
0.1%
825 4
0.1%

fico_range_high
Real number (ℝ)

Distinct38
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean702.8452
Minimum664
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:36.139848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum664
5-th percentile664
Q1679
median694
Q3719
95-th percentile769
Maximum850
Range186
Interquartile range (IQR)40

Descriptive statistics

Standard deviation33.2386913
Coefficient of variation (CV)0.04729162452
Kurtosis1.411955623
Mean702.8452
Median Absolute Deviation (MAD)20
Skewness1.202127878
Sum3514226
Variance1104.810599
MonotonicityNot monotonic
2023-07-21T13:17:36.226952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
664 433
 
8.7%
674 403
 
8.1%
669 392
 
7.8%
684 364
 
7.3%
679 334
 
6.7%
689 316
 
6.3%
694 316
 
6.3%
704 290
 
5.8%
699 289
 
5.8%
709 247
 
4.9%
Other values (28) 1616
32.3%
ValueCountFrequency (%)
664 433
8.7%
669 392
7.8%
674 403
8.1%
679 334
6.7%
684 364
7.3%
ValueCountFrequency (%)
850 1
 
< 0.1%
844 3
0.1%
839 3
0.1%
834 3
0.1%
829 4
0.1%

inq_last_6mths
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5578
Minimum0
Maximum5
Zeros3053
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:36.304950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8416655156
Coefficient of variation (CV)1.508901964
Kurtosis3.65907758
Mean0.5578
Median Absolute Deviation (MAD)0
Skewness1.779295801
Sum2789
Variance0.7084008402
MonotonicityNot monotonic
2023-07-21T13:17:36.373859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 3053
61.1%
1 1346
26.9%
2 417
 
8.3%
3 141
 
2.8%
4 29
 
0.6%
5 14
 
0.3%
ValueCountFrequency (%)
0 3053
61.1%
1 1346
26.9%
2 417
 
8.3%
3 141
 
2.8%
4 29
 
0.6%
ValueCountFrequency (%)
5 14
 
0.3%
4 29
 
0.6%
3 141
 
2.8%
2 417
 
8.3%
1 1346
26.9%

mths_since_last_delinq
Real number (ℝ)

Distinct94
Distinct (%)3.8%
Missing2556
Missing (%)51.1%
Infinite0
Infinite (%)0.0%
Mean34.48076923
Minimum0
Maximum113
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:36.462853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q116
median31
Q350.25
95-th percentile74
Maximum113
Range113
Interquartile range (IQR)34.25

Descriptive statistics

Standard deviation21.83246686
Coefficient of variation (CV)0.6331780684
Kurtosis-0.7364621882
Mean34.48076923
Median Absolute Deviation (MAD)16
Skewness0.4733855051
Sum84271
Variance476.6566092
MonotonicityNot monotonic
2023-07-21T13:17:36.560058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 55
 
1.1%
13 52
 
1.0%
28 52
 
1.0%
12 50
 
1.0%
22 49
 
1.0%
15 48
 
1.0%
6 47
 
0.9%
17 46
 
0.9%
31 46
 
0.9%
19 45
 
0.9%
Other values (84) 1954
39.1%
(Missing) 2556
51.1%
ValueCountFrequency (%)
0 5
 
0.1%
1 17
0.3%
2 18
0.4%
3 24
0.5%
4 34
0.7%
ValueCountFrequency (%)
113 1
< 0.1%
100 1
< 0.1%
97 1
< 0.1%
94 2
< 0.1%
93 1
< 0.1%

mths_since_last_record
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct111
Distinct (%)14.2%
Missing4220
Missing (%)84.4%
Infinite0
Infinite (%)0.0%
Mean73.88076923
Minimum0
Maximum120
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:36.657737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q157
median74
Q394
95-th percentile114
Maximum120
Range120
Interquartile range (IQR)37

Descriptive statistics

Standard deviation25.70360113
Coefficient of variation (CV)0.3479065175
Kurtosis-0.4176827651
Mean73.88076923
Median Absolute Deviation (MAD)18
Skewness-0.3001895758
Sum57627
Variance660.6751111
MonotonicityNot monotonic
2023-07-21T13:17:36.759463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 18
 
0.4%
81 17
 
0.3%
67 17
 
0.3%
74 16
 
0.3%
72 15
 
0.3%
78 15
 
0.3%
80 14
 
0.3%
71 14
 
0.3%
90 14
 
0.3%
56 14
 
0.3%
Other values (101) 626
 
12.5%
(Missing) 4220
84.4%
ValueCountFrequency (%)
0 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
ValueCountFrequency (%)
120 1
 
< 0.1%
119 3
 
0.1%
118 2
 
< 0.1%
117 13
0.3%
116 7
0.1%

open_acc
Real number (ℝ)

Distinct44
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5914
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:36.859257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q18
median11
Q314
95-th percentile22
Maximum56
Range55
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.631908177
Coefficient of variation (CV)0.4858695393
Kurtosis3.224286909
Mean11.5914
Median Absolute Deviation (MAD)3
Skewness1.326342014
Sum57957
Variance31.71838972
MonotonicityNot monotonic
2023-07-21T13:17:36.960826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
9 455
 
9.1%
8 446
 
8.9%
10 414
 
8.3%
11 382
 
7.6%
7 380
 
7.6%
12 326
 
6.5%
13 311
 
6.2%
6 303
 
6.1%
14 270
 
5.4%
5 240
 
4.8%
Other values (34) 1473
29.5%
ValueCountFrequency (%)
1 5
 
0.1%
2 16
 
0.3%
3 74
 
1.5%
4 156
3.1%
5 240
4.8%
ValueCountFrequency (%)
56 1
< 0.1%
47 1
< 0.1%
45 1
< 0.1%
43 1
< 0.1%
42 1
< 0.1%

pub_rec
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1946
Minimum0
Maximum7
Zeros4221
Zeros (%)84.4%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:37.041244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5381345164
Coefficient of variation (CV)2.765336672
Kurtosis31.33751439
Mean0.1946
Median Absolute Deviation (MAD)0
Skewness4.477871315
Sum973
Variance0.2895887578
MonotonicityNot monotonic
2023-07-21T13:17:37.106780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4221
84.4%
1 662
 
13.2%
2 75
 
1.5%
3 25
 
0.5%
4 6
 
0.1%
5 5
 
0.1%
6 5
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 4221
84.4%
1 662
 
13.2%
2 75
 
1.5%
3 25
 
0.5%
4 6
 
0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 5
 
0.1%
5 5
 
0.1%
4 6
 
0.1%
3 25
0.5%

revol_bal
Real number (ℝ)

Distinct4619
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16980.4988
Minimum0
Maximum364270
Zeros25
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:37.196632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1619.9
Q15942
median11549.5
Q320667.5
95-th percentile45696.6
Maximum364270
Range364270
Interquartile range (IQR)14725.5

Descriptive statistics

Standard deviation21555.93003
Coefficient of variation (CV)1.26945211
Kurtosis51.72925879
Mean16980.4988
Median Absolute Deviation (MAD)6543
Skewness5.610626518
Sum84902494
Variance464658119.7
MonotonicityNot monotonic
2023-07-21T13:17:37.294014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
0.5%
4770 4
 
0.1%
5305 3
 
0.1%
15772 3
 
0.1%
5749 3
 
0.1%
6956 3
 
0.1%
16655 3
 
0.1%
5470 3
 
0.1%
2801 3
 
0.1%
5829 3
 
0.1%
Other values (4609) 4947
98.9%
ValueCountFrequency (%)
0 25
0.5%
5 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
364270 1
< 0.1%
322318 1
< 0.1%
276543 1
< 0.1%
266867 1
< 0.1%
255805 1
< 0.1%

revol_util
Real number (ℝ)

Distinct989
Distinct (%)19.8%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean50.34429544
Minimum0
Maximum114.7
Zeros26
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:37.397162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.4
Q131.5
median50
Q369.925
95-th percentile91.325
Maximum114.7
Range114.7
Interquartile range (IQR)38.425

Descriptive statistics

Standard deviation24.84128553
Coefficient of variation (CV)0.4934280104
Kurtosis-0.857571639
Mean50.34429544
Median Absolute Deviation (MAD)19.2
Skewness-0.006564710598
Sum251520.1
Variance617.089467
MonotonicityNot monotonic
2023-07-21T13:17:37.497411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
 
0.5%
35 15
 
0.3%
47.2 15
 
0.3%
39 14
 
0.3%
70 14
 
0.3%
41.4 14
 
0.3%
78.9 14
 
0.3%
52.7 14
 
0.3%
38 13
 
0.3%
59.2 13
 
0.3%
Other values (979) 4844
96.9%
ValueCountFrequency (%)
0 26
0.5%
0.1 1
 
< 0.1%
0.2 4
 
0.1%
0.3 6
 
0.1%
0.4 1
 
< 0.1%
ValueCountFrequency (%)
114.7 1
< 0.1%
107.4 1
< 0.1%
105.4 1
< 0.1%
102.6 1
< 0.1%
102.5 1
< 0.1%

total_acc
Real number (ℝ)

Distinct78
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.9644
Minimum2
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:37.605605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q115
median22
Q330
95-th percentile47
Maximum89
Range87
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.80709169
Coefficient of variation (CV)0.4926929816
Kurtosis1.319153068
Mean23.9644
Median Absolute Deviation (MAD)8
Skewness0.9766833596
Sum119822
Variance139.4074141
MonotonicityNot monotonic
2023-07-21T13:17:37.697996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 194
 
3.9%
16 192
 
3.8%
14 191
 
3.8%
18 190
 
3.8%
22 187
 
3.7%
17 181
 
3.6%
24 181
 
3.6%
15 171
 
3.4%
23 170
 
3.4%
20 169
 
3.4%
Other values (68) 3174
63.5%
ValueCountFrequency (%)
2 3
 
0.1%
3 11
 
0.2%
4 24
0.5%
5 33
0.7%
6 37
0.7%
ValueCountFrequency (%)
89 1
< 0.1%
88 1
< 0.1%
82 1
< 0.1%
80 1
< 0.1%
78 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
w
3458 
f
1542 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roww
2nd roww
3rd roww
4th rowf
5th roww

Common Values

ValueCountFrequency (%)
w 3458
69.2%
f 1542
30.8%

Length

2023-07-21T13:17:37.782251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:37.861253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
w 3458
69.2%
f 1542
30.8%

Most occurring characters

ValueCountFrequency (%)
w 3458
69.2%
f 1542
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5000
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 3458
69.2%
f 1542
30.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 5000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 3458
69.2%
f 1542
30.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 3458
69.2%
f 1542
30.8%

out_prncp
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2086
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4383.980162
Minimum0
Maximum38981.88
Zeros2872
Zeros (%)57.4%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:37.939481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36464.28
95-th percentile21103.768
Maximum38981.88
Range38981.88
Interquartile range (IQR)6464.28

Descriptive statistics

Standard deviation7446.874754
Coefficient of variation (CV)1.698656125
Kurtosis3.881326773
Mean4383.980162
Median Absolute Deviation (MAD)0
Skewness2.033006782
Sum21919900.81
Variance55455943.6
MonotonicityNot monotonic
2023-07-21T13:17:38.040376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2872
57.4%
4812.75 3
 
0.1%
7021.05 3
 
0.1%
9326.86 2
 
< 0.1%
15293.93 2
 
< 0.1%
4502.23 2
 
< 0.1%
8173.58 2
 
< 0.1%
6068.03 2
 
< 0.1%
9238.49 2
 
< 0.1%
9063.29 2
 
< 0.1%
Other values (2076) 2108
42.2%
ValueCountFrequency (%)
0 2872
57.4%
0.5 1
 
< 0.1%
2.73 1
 
< 0.1%
6.33 1
 
< 0.1%
7.22 1
 
< 0.1%
ValueCountFrequency (%)
38981.88 1
< 0.1%
38508.17 1
< 0.1%
38160.4 1
< 0.1%
37598.13 1
< 0.1%
37472.22 1
< 0.1%

out_prncp_inv
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2092
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4382.858138
Minimum0
Maximum38981.88
Zeros2872
Zeros (%)57.4%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:38.148969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36464.28
95-th percentile21103.768
Maximum38981.88
Range38981.88
Interquartile range (IQR)6464.28

Descriptive statistics

Standard deviation7445.368689
Coefficient of variation (CV)1.69874736
Kurtosis3.882636797
Mean4382.858138
Median Absolute Deviation (MAD)0
Skewness2.033241984
Sum21914290.69
Variance55433514.92
MonotonicityNot monotonic
2023-07-21T13:17:38.568644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2872
57.4%
4812.75 3
 
0.1%
7021.05 3
 
0.1%
1219.77 2
 
< 0.1%
4502.23 2
 
< 0.1%
8457.66 2
 
< 0.1%
9063.29 2
 
< 0.1%
8173.58 2
 
< 0.1%
12637.97 2
 
< 0.1%
8196.01 2
 
< 0.1%
Other values (2082) 2108
42.2%
ValueCountFrequency (%)
0 2872
57.4%
0.5 1
 
< 0.1%
2.73 1
 
< 0.1%
6.33 1
 
< 0.1%
7.22 1
 
< 0.1%
ValueCountFrequency (%)
38981.88 1
< 0.1%
38508.17 1
< 0.1%
38160.4 1
< 0.1%
37598.13 1
< 0.1%
37472.22 1
< 0.1%

total_pymnt
Real number (ℝ)

Distinct4979
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11982.34895
Minimum0
Maximum59538.05303
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:38.674186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1382.4015
Q14614.225
median9172.285
Q316550.46436
95-th percentile32900.10795
Maximum59538.05303
Range59538.05303
Interquartile range (IQR)11936.23936

Descriptive statistics

Standard deviation9935.192714
Coefficient of variation (CV)0.8291523435
Kurtosis1.614727716
Mean11982.34895
Median Absolute Deviation (MAD)5419.619034
Skewness1.343240727
Sum59911744.75
Variance98708054.25
MonotonicityNot monotonic
2023-07-21T13:17:38.766961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4283.83 3
 
0.1%
2832.39 2
 
< 0.1%
6932.32 2
 
< 0.1%
2126.25 2
 
< 0.1%
1520.21 2
 
< 0.1%
18000 2
 
< 0.1%
3916.98 2
 
< 0.1%
4724.9 2
 
< 0.1%
4916.27 2
 
< 0.1%
7376.17 2
 
< 0.1%
Other values (4969) 4979
99.6%
ValueCountFrequency (%)
0 1
< 0.1%
171.25 1
< 0.1%
210.2 1
< 0.1%
218.45 1
< 0.1%
229.62 1
< 0.1%
ValueCountFrequency (%)
59538.05303 1
< 0.1%
58299.05994 1
< 0.1%
57716.29985 1
< 0.1%
56666.22584 1
< 0.1%
53749.39982 1
< 0.1%

total_pymnt_inv
Real number (ℝ)

Distinct4971
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11973.05408
Minimum0
Maximum59538.05
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:38.868804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1377.595
Q14613.11
median9159.83
Q316548.3175
95-th percentile32900.1065
Maximum59538.05
Range59538.05
Interquartile range (IQR)11935.2075

Descriptive statistics

Standard deviation9932.066043
Coefficient of variation (CV)0.8295348852
Kurtosis1.615774336
Mean11973.05408
Median Absolute Deviation (MAD)5414.89
Skewness1.343926162
Sum59865270.41
Variance98645935.89
MonotonicityNot monotonic
2023-07-21T13:17:38.963001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4283.83 3
 
0.1%
7042.39 3
 
0.1%
4724.9 2
 
< 0.1%
41522.91 2
 
< 0.1%
2809.19 2
 
< 0.1%
2832.39 2
 
< 0.1%
2126.25 2
 
< 0.1%
1520.21 2
 
< 0.1%
3916.98 2
 
< 0.1%
5473.42 2
 
< 0.1%
Other values (4961) 4978
99.6%
ValueCountFrequency (%)
0 1
< 0.1%
171.25 1
< 0.1%
210.2 1
< 0.1%
218.45 1
< 0.1%
229.62 1
< 0.1%
ValueCountFrequency (%)
59538.05 1
< 0.1%
58299.06 1
< 0.1%
57633.85 1
< 0.1%
56666.23 1
< 0.1%
53749.4 1
< 0.1%

total_rec_prncp
Real number (ℝ)

Distinct3122
Distinct (%)62.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9331.934926
Minimum0
Maximum40000
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:39.068294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile850.128
Q13000
median6615.93
Q313041.685
95-th percentile27000
Maximum40000
Range40000
Interquartile range (IQR)10041.685

Descriptive statistics

Standard deviation8292.918847
Coefficient of variation (CV)0.8886601667
Kurtosis1.415348035
Mean9331.934926
Median Absolute Deviation (MAD)4319.075
Skewness1.342716811
Sum46659674.63
Variance68772503
MonotonicityNot monotonic
2023-07-21T13:17:39.170506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 157
 
3.1%
12000 127
 
2.5%
15000 117
 
2.3%
20000 109
 
2.2%
5000 105
 
2.1%
35000 89
 
1.8%
8000 86
 
1.7%
6000 77
 
1.5%
16000 59
 
1.2%
24000 55
 
1.1%
Other values (3112) 4019
80.4%
ValueCountFrequency (%)
0 6
0.1%
114.49 1
 
< 0.1%
118.81 1
 
< 0.1%
148.51 1
 
< 0.1%
151.71 1
 
< 0.1%
ValueCountFrequency (%)
40000 16
0.3%
38400 1
 
< 0.1%
38000 1
 
< 0.1%
36318.79 1
 
< 0.1%
36250 1
 
< 0.1%

total_rec_int
Real number (ℝ)

Distinct4944
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2491.426416
Minimum0
Maximum26216.92
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:39.274552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile214.5955
Q1739.1425
median1537.7
Q33238.1075
95-th percentile8038.633
Maximum26216.92
Range26216.92
Interquartile range (IQR)2498.965

Descriptive statistics

Standard deviation2755.913306
Coefficient of variation (CV)1.106158821
Kurtosis9.706174924
Mean2491.426416
Median Absolute Deviation (MAD)982.925
Skewness2.573531135
Sum12457132.08
Variance7595058.149
MonotonicityNot monotonic
2023-07-21T13:17:39.375773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
0.2%
1096.58 3
 
0.1%
620.2 3
 
0.1%
1442.26 2
 
< 0.1%
637.96 2
 
< 0.1%
679.89 2
 
< 0.1%
5595.54 2
 
< 0.1%
56.76 2
 
< 0.1%
234.48 2
 
< 0.1%
538.29 2
 
< 0.1%
Other values (4934) 4972
99.4%
ValueCountFrequency (%)
0 8
0.2%
0.84 1
 
< 0.1%
1.8 1
 
< 0.1%
2.21 1
 
< 0.1%
2.52 1
 
< 0.1%
ValueCountFrequency (%)
26216.92 1
< 0.1%
24999.06 1
< 0.1%
24538.05 1
< 0.1%
22716.3 1
< 0.1%
22305.91 1
< 0.1%

total_rec_late_fee
Real number (ℝ)

Distinct165
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.852191354
Minimum0
Maximum274
Zeros4775
Zeros (%)95.5%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:39.478088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum274
Range274
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.54220949
Coefficient of variation (CV)6.771551685
Kurtosis205.0684908
Mean1.852191354
Median Absolute Deviation (MAD)0
Skewness12.41457103
Sum9260.956771
Variance157.3070188
MonotonicityNot monotonic
2023-07-21T13:17:39.584535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4775
95.5%
15 48
 
1.0%
30 10
 
0.2%
18.07 3
 
0.1%
45 2
 
< 0.1%
31.65 2
 
< 0.1%
23.26 2
 
< 0.1%
19.28 1
 
< 0.1%
25.76 1
 
< 0.1%
29.4 1
 
< 0.1%
Other values (155) 155
 
3.1%
ValueCountFrequency (%)
0 4775
95.5%
15 48
 
1.0%
15 1
 
< 0.1%
15 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
274 1
< 0.1%
268.8 1
< 0.1%
263.75 1
< 0.1%
256.3 1
< 0.1%
203.28 1
< 0.1%

recoveries
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct443
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.135486
Minimum0
Maximum11443.52
Zeros4552
Zeros (%)91.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:39.684386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1066.866
Maximum11443.52
Range11443.52
Interquartile range (IQR)0

Descriptive statistics

Standard deviation703.2547343
Coefficient of variation (CV)4.475467332
Kurtosis55.91781629
Mean157.135486
Median Absolute Deviation (MAD)0
Skewness6.563219971
Sum785677.43
Variance494567.2214
MonotonicityNot monotonic
2023-07-21T13:17:39.784649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4552
91.0%
50 3
 
0.1%
47.97 2
 
< 0.1%
1600 2
 
< 0.1%
150 2
 
< 0.1%
250 2
 
< 0.1%
1441.68 1
 
< 0.1%
1188.01 1
 
< 0.1%
1039.46 1
 
< 0.1%
1738.23 1
 
< 0.1%
Other values (433) 433
 
8.7%
ValueCountFrequency (%)
0 4552
91.0%
0.06 1
 
< 0.1%
0.36 1
 
< 0.1%
0.43 1
 
< 0.1%
0.98 1
 
< 0.1%
ValueCountFrequency (%)
11443.52 1
< 0.1%
9250 1
< 0.1%
9100 1
< 0.1%
8300 1
< 0.1%
7619.85 1
< 0.1%

collection_recovery_fee
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct432
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.59876504
Minimum0
Maximum2059.8336
Zeros4567
Zeros (%)91.3%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:39.887625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile179.68428
Maximum2059.8336
Range2059.8336
Interquartile range (IQR)0

Descriptive statistics

Standard deviation122.8311303
Coefficient of variation (CV)4.617926063
Kurtosis62.12947805
Mean26.59876504
Median Absolute Deviation (MAD)0
Skewness6.907627869
Sum132993.8252
Variance15087.48658
MonotonicityNot monotonic
2023-07-21T13:17:39.989450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4567
91.3%
9 2
 
< 0.1%
45 2
 
< 0.1%
417.5748 1
 
< 0.1%
187.1028 1
 
< 0.1%
312.8814 1
 
< 0.1%
546.4098 1
 
< 0.1%
122.8068 1
 
< 0.1%
164.0484 1
 
< 0.1%
440.6598 1
 
< 0.1%
Other values (422) 422
 
8.4%
ValueCountFrequency (%)
0 4567
91.3%
0.8648 1
 
< 0.1%
0.8939 1
 
< 0.1%
1.45 1
 
< 0.1%
2.419200003 1
 
< 0.1%
ValueCountFrequency (%)
2059.8336 1
< 0.1%
1665 1
< 0.1%
1638 1
< 0.1%
1494 1
< 0.1%
1371.573 1
< 0.1%

last_pymnt_d
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct91
Distinct (%)1.8%
Missing6
Missing (%)0.1%
Memory size78.1 KiB
Mar-2019
2005 
Feb-2019
 
220
Feb-2018
 
87
Aug-2017
 
86
Apr-2018
 
86
Other values (86)
2510 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters39952
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st rowMar-2019
2nd rowAug-2018
3rd rowMar-2019
4th rowJan-2019
5th rowNov-2018

Common Values

ValueCountFrequency (%)
Mar-2019 2005
40.1%
Feb-2019 220
 
4.4%
Feb-2018 87
 
1.7%
Aug-2017 86
 
1.7%
Apr-2018 86
 
1.7%
Aug-2018 83
 
1.7%
Jul-2018 82
 
1.6%
Mar-2018 81
 
1.6%
Nov-2018 80
 
1.6%
Jan-2018 78
 
1.6%
Other values (81) 2106
42.1%

Length

2023-07-21T13:17:40.088206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mar-2019 2005
40.1%
feb-2019 220
 
4.4%
feb-2018 87
 
1.7%
aug-2017 86
 
1.7%
apr-2018 86
 
1.7%
aug-2018 83
 
1.7%
jul-2018 82
 
1.6%
mar-2018 81
 
1.6%
nov-2018 80
 
1.6%
jan-2018 78
 
1.6%
Other values (81) 2106
42.2%

Most occurring characters

ValueCountFrequency (%)
2 5016
12.6%
1 5001
12.5%
0 4998
12.5%
- 4994
12.5%
a 2687
 
6.7%
r 2432
 
6.1%
M 2426
 
6.1%
9 2301
 
5.8%
8 917
 
2.3%
e 895
 
2.2%
Other values (23) 8285
20.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19976
50.0%
Lowercase Letter 9988
25.0%
Dash Punctuation 4994
 
12.5%
Uppercase Letter 4994
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2687
26.9%
r 2432
24.3%
e 895
 
9.0%
u 719
 
7.2%
n 482
 
4.8%
c 479
 
4.8%
b 432
 
4.3%
p 406
 
4.1%
g 276
 
2.8%
o 271
 
2.7%
Other values (4) 909
 
9.1%
Decimal Number
ValueCountFrequency (%)
2 5016
25.1%
1 5001
25.0%
0 4998
25.0%
9 2301
11.5%
8 917
 
4.6%
7 742
 
3.7%
6 528
 
2.6%
5 325
 
1.6%
4 104
 
0.5%
3 44
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
M 2426
48.6%
J 704
 
14.1%
A 468
 
9.4%
F 432
 
8.7%
N 271
 
5.4%
D 249
 
5.0%
O 230
 
4.6%
S 214
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 4994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24970
62.5%
Latin 14982
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2687
17.9%
r 2432
16.2%
M 2426
16.2%
e 895
 
6.0%
u 719
 
4.8%
J 704
 
4.7%
n 482
 
3.2%
c 479
 
3.2%
A 468
 
3.1%
b 432
 
2.9%
Other values (12) 3258
21.7%
Common
ValueCountFrequency (%)
2 5016
20.1%
1 5001
20.0%
0 4998
20.0%
- 4994
20.0%
9 2301
9.2%
8 917
 
3.7%
7 742
 
3.0%
6 528
 
2.1%
5 325
 
1.3%
4 104
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5016
12.6%
1 5001
12.5%
0 4998
12.5%
- 4994
12.5%
a 2687
 
6.7%
r 2432
 
6.1%
M 2426
 
6.1%
9 2301
 
5.8%
8 917
 
2.3%
e 895
 
2.2%
Other values (23) 8285
20.7%

last_pymnt_amnt
Real number (ℝ)

Distinct4472
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3202.836304
Minimum0
Maximum40351.17
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:40.189230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile98.776
Q1301.9825
median570.365
Q33182.15
95-th percentile15727.875
Maximum40351.17
Range40351.17
Interquartile range (IQR)2880.1675

Descriptive statistics

Standard deviation5821.191655
Coefficient of variation (CV)1.81751145
Kurtosis8.469177842
Mean3202.836304
Median Absolute Deviation (MAD)356.995
Skewness2.756012511
Sum16014181.52
Variance33886272.28
MonotonicityNot monotonic
2023-07-21T13:17:40.296373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 8
 
0.2%
0 8
 
0.2%
326.97 6
 
0.1%
304.72 6
 
0.1%
500 5
 
0.1%
301.15 5
 
0.1%
307.27 5
 
0.1%
339.31 5
 
0.1%
324.65 5
 
0.1%
541.42 5
 
0.1%
Other values (4462) 4942
98.8%
ValueCountFrequency (%)
0 8
0.2%
0.11 1
 
< 0.1%
0.13 1
 
< 0.1%
0.42 1
 
< 0.1%
0.49 1
 
< 0.1%
ValueCountFrequency (%)
40351.17 1
< 0.1%
40194.25 1
< 0.1%
38562.32 1
< 0.1%
38546.99 1
< 0.1%
38537.16 1
< 0.1%

next_pymnt_d
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing2863
Missing (%)57.3%
Memory size78.1 KiB
Apr-2019
2136 
Dec-2012
 
1

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters17096
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowApr-2019
2nd rowApr-2019
3rd rowApr-2019
4th rowApr-2019
5th rowApr-2019

Common Values

ValueCountFrequency (%)
Apr-2019 2136
42.7%
Dec-2012 1
 
< 0.1%
(Missing) 2863
57.3%

Length

2023-07-21T13:17:40.392159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:40.472505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
apr-2019 2136
> 99.9%
dec-2012 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 2138
12.5%
- 2137
12.5%
0 2137
12.5%
1 2137
12.5%
A 2136
12.5%
p 2136
12.5%
r 2136
12.5%
9 2136
12.5%
D 1
 
< 0.1%
e 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8548
50.0%
Lowercase Letter 4274
25.0%
Dash Punctuation 2137
 
12.5%
Uppercase Letter 2137
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2138
25.0%
0 2137
25.0%
1 2137
25.0%
9 2136
25.0%
Lowercase Letter
ValueCountFrequency (%)
p 2136
50.0%
r 2136
50.0%
e 1
 
< 0.1%
c 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
A 2136
> 99.9%
D 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 2137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10685
62.5%
Latin 6411
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2136
33.3%
p 2136
33.3%
r 2136
33.3%
D 1
 
< 0.1%
e 1
 
< 0.1%
c 1
 
< 0.1%
Common
ValueCountFrequency (%)
2 2138
20.0%
- 2137
20.0%
0 2137
20.0%
1 2137
20.0%
9 2136
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2138
12.5%
- 2137
12.5%
0 2137
12.5%
1 2137
12.5%
A 2136
12.5%
p 2136
12.5%
r 2136
12.5%
9 2136
12.5%
D 1
 
< 0.1%
e 1
 
< 0.1%

last_credit_pull_d
Categorical

HIGH CARDINALITY  HIGH CORRELATION  IMBALANCE 

Distinct80
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Mar-2019
3108 
Feb-2019
 
164
Jul-2018
 
131
Jan-2019
 
130
Dec-2018
 
108
Other values (75)
1359 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters40000
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.5%

Sample

1st rowMar-2019
2nd rowSep-2017
3rd rowMar-2019
4th rowJan-2019
5th rowNov-2018

Common Values

ValueCountFrequency (%)
Mar-2019 3108
62.2%
Feb-2019 164
 
3.3%
Jul-2018 131
 
2.6%
Jan-2019 130
 
2.6%
Dec-2018 108
 
2.2%
Oct-2016 105
 
2.1%
Oct-2018 98
 
2.0%
Aug-2018 93
 
1.9%
Sep-2018 85
 
1.7%
Nov-2018 81
 
1.6%
Other values (70) 897
 
17.9%

Length

2023-07-21T13:17:40.540022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mar-2019 3108
62.2%
feb-2019 164
 
3.3%
jul-2018 131
 
2.6%
jan-2019 130
 
2.6%
dec-2018 108
 
2.2%
oct-2016 105
 
2.1%
oct-2018 98
 
2.0%
aug-2018 93
 
1.9%
sep-2018 85
 
1.7%
nov-2018 81
 
1.6%
Other values (70) 897
 
17.9%

Most occurring characters

ValueCountFrequency (%)
2 5004
12.5%
1 5003
12.5%
0 5002
12.5%
- 5000
12.5%
a 3501
8.8%
9 3403
8.5%
r 3312
8.3%
M 3287
8.2%
8 860
 
2.1%
e 608
 
1.5%
Other values (23) 5020
12.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
50.0%
Lowercase Letter 10000
25.0%
Dash Punctuation 5000
 
12.5%
Uppercase Letter 5000
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3501
35.0%
r 3312
33.1%
e 608
 
6.1%
c 436
 
4.4%
u 384
 
3.8%
b 290
 
2.9%
n 268
 
2.7%
t 259
 
2.6%
p 248
 
2.5%
l 179
 
1.8%
Other values (4) 515
 
5.1%
Decimal Number
ValueCountFrequency (%)
2 5004
25.0%
1 5003
25.0%
0 5002
25.0%
9 3403
17.0%
8 860
 
4.3%
7 423
 
2.1%
6 216
 
1.1%
5 57
 
0.3%
4 22
 
0.1%
3 10
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M 3287
65.7%
J 447
 
8.9%
F 290
 
5.8%
O 259
 
5.2%
A 258
 
5.2%
D 177
 
3.5%
S 141
 
2.8%
N 141
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25000
62.5%
Latin 15000
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3501
23.3%
r 3312
22.1%
M 3287
21.9%
e 608
 
4.1%
J 447
 
3.0%
c 436
 
2.9%
u 384
 
2.6%
b 290
 
1.9%
F 290
 
1.9%
n 268
 
1.8%
Other values (12) 2177
14.5%
Common
ValueCountFrequency (%)
2 5004
20.0%
1 5003
20.0%
0 5002
20.0%
- 5000
20.0%
9 3403
13.6%
8 860
 
3.4%
7 423
 
1.7%
6 216
 
0.9%
5 57
 
0.2%
4 22
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5004
12.5%
1 5003
12.5%
0 5002
12.5%
- 5000
12.5%
a 3501
8.8%
9 3403
8.5%
r 3312
8.3%
M 3287
8.2%
8 860
 
2.1%
e 608
 
1.5%
Other values (23) 5020
12.6%

last_fico_range_high
Real number (ℝ)

Distinct71
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean686.9902
Minimum499
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:40.628759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum499
5-th percentile534
Q1654
median699
Q3734
95-th percentile794
Maximum850
Range351
Interquartile range (IQR)80

Descriptive statistics

Standard deviation74.88822707
Coefficient of variation (CV)0.1090091635
Kurtosis0.01445926605
Mean686.9902
Median Absolute Deviation (MAD)40
Skewness-0.6620735184
Sum3434951
Variance5608.246553
MonotonicityNot monotonic
2023-07-21T13:17:40.727298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
709 179
 
3.6%
704 176
 
3.5%
694 175
 
3.5%
724 174
 
3.5%
684 172
 
3.4%
674 170
 
3.4%
714 168
 
3.4%
689 167
 
3.3%
699 163
 
3.3%
729 153
 
3.1%
Other values (61) 3303
66.1%
ValueCountFrequency (%)
499 91
1.8%
504 14
 
0.3%
509 21
 
0.4%
514 30
 
0.6%
519 21
 
0.4%
ValueCountFrequency (%)
850 1
 
< 0.1%
844 2
 
< 0.1%
839 2
 
< 0.1%
834 9
0.2%
829 17
0.3%

last_fico_range_low
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean673.981
Minimum0
Maximum845
Zeros91
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:40.838197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile530
Q1650
median695
Q3730
95-th percentile790
Maximum845
Range845
Interquartile range (IQR)80

Descriptive statistics

Standard deviation115.6513367
Coefficient of variation (CV)0.1715943576
Kurtosis18.40487723
Mean673.981
Median Absolute Deviation (MAD)40
Skewness-3.618087377
Sum3369905
Variance13375.23169
MonotonicityNot monotonic
2023-07-21T13:17:40.933218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
705 179
 
3.6%
700 176
 
3.5%
690 175
 
3.5%
720 174
 
3.5%
680 172
 
3.4%
670 170
 
3.4%
710 168
 
3.4%
685 167
 
3.3%
695 163
 
3.3%
725 153
 
3.1%
Other values (61) 3303
66.1%
ValueCountFrequency (%)
0 91
1.8%
500 14
 
0.3%
505 21
 
0.4%
510 30
 
0.6%
515 21
 
0.4%
ValueCountFrequency (%)
845 1
 
< 0.1%
840 2
 
< 0.1%
835 2
 
< 0.1%
830 9
0.2%
825 17
0.3%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
0.0
4895 
1.0
 
98
2.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15000
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4895
97.9%
1.0 98
 
2.0%
2.0 7
 
0.1%

Length

2023-07-21T13:17:41.017194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:41.095673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4895
97.9%
1.0 98
 
2.0%
2.0 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 9895
66.0%
. 5000
33.3%
1 98
 
0.7%
2 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
66.7%
Other Punctuation 5000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9895
99.0%
1 98
 
1.0%
2 7
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9895
66.0%
. 5000
33.3%
1 98
 
0.7%
2 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9895
66.0%
. 5000
33.3%
1 98
 
0.7%
2 7
 
< 0.1%

mths_since_last_major_derog
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct95
Distinct (%)7.2%
Missing3688
Missing (%)73.8%
Infinite0
Infinite (%)0.0%
Mean43.97637195
Minimum1
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:41.175567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q127.75
median43.5
Q361
95-th percentile76
Maximum118
Range117
Interquartile range (IQR)33.25

Descriptive statistics

Standard deviation21.09931236
Coefficient of variation (CV)0.4797874728
Kurtosis-0.7385938158
Mean43.97637195
Median Absolute Deviation (MAD)16.5
Skewness0.05400569565
Sum57697
Variance445.1809821
MonotonicityNot monotonic
2023-07-21T13:17:41.277046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 28
 
0.6%
36 28
 
0.6%
54 28
 
0.6%
52 28
 
0.6%
33 26
 
0.5%
45 24
 
0.5%
50 24
 
0.5%
67 24
 
0.5%
68 24
 
0.5%
40 24
 
0.5%
Other values (85) 1054
 
21.1%
(Missing) 3688
73.8%
ValueCountFrequency (%)
1 3
 
0.1%
2 4
0.1%
3 7
0.1%
4 3
 
0.1%
5 8
0.2%
ValueCountFrequency (%)
118 1
 
< 0.1%
113 1
 
< 0.1%
100 1
 
< 0.1%
99 1
 
< 0.1%
94 3
0.1%

policy_code
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
1.0
5000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5000
100.0%

Length

2023-07-21T13:17:41.365909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:41.447068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5000
33.3%
. 5000
33.3%
0 5000
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
66.7%
Other Punctuation 5000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5000
50.0%
0 5000
50.0%
Other Punctuation
ValueCountFrequency (%)
. 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5000
33.3%
. 5000
33.3%
0 5000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5000
33.3%
. 5000
33.3%
0 5000
33.3%

application_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Individual
4706 
Joint App
 
294

Length

Max length10
Median length10
Mean length9.9412
Min length9

Characters and Unicode

Total characters49706
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowJoint App
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual 4706
94.1%
Joint App 294
 
5.9%

Length

2023-07-21T13:17:41.507608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:41.585446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
individual 4706
88.9%
joint 294
 
5.6%
app 294
 
5.6%

Most occurring characters

ValueCountFrequency (%)
i 9706
19.5%
d 9412
18.9%
n 5000
10.1%
I 4706
9.5%
v 4706
9.5%
u 4706
9.5%
a 4706
9.5%
l 4706
9.5%
p 588
 
1.2%
J 294
 
0.6%
Other values (4) 1176
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44118
88.8%
Uppercase Letter 5294
 
10.7%
Space Separator 294
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9706
22.0%
d 9412
21.3%
n 5000
11.3%
v 4706
10.7%
u 4706
10.7%
a 4706
10.7%
l 4706
10.7%
p 588
 
1.3%
o 294
 
0.7%
t 294
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
I 4706
88.9%
J 294
 
5.6%
A 294
 
5.6%
Space Separator
ValueCountFrequency (%)
294
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49412
99.4%
Common 294
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9706
19.6%
d 9412
19.0%
n 5000
10.1%
I 4706
9.5%
v 4706
9.5%
u 4706
9.5%
a 4706
9.5%
l 4706
9.5%
p 588
 
1.2%
J 294
 
0.6%
Other values (3) 882
 
1.8%
Common
ValueCountFrequency (%)
294
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49706
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9706
19.5%
d 9412
18.9%
n 5000
10.1%
I 4706
9.5%
v 4706
9.5%
u 4706
9.5%
a 4706
9.5%
l 4706
9.5%
p 588
 
1.2%
J 294
 
0.6%
Other values (4) 1176
 
2.4%

acc_now_delinq
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
0.0
4978 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4978
99.6%
1.0 22
 
0.4%

Length

2023-07-21T13:17:41.647870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:41.722712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4978
99.6%
1.0 22
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 9978
66.5%
. 5000
33.3%
1 22
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
66.7%
Other Punctuation 5000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9978
99.8%
1 22
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9978
66.5%
. 5000
33.3%
1 22
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9978
66.5%
. 5000
33.3%
1 22
 
0.1%

tot_coll_amt
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct577
Distinct (%)11.8%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean216.2
Minimum0
Maximum25387
Zeros4112
Zeros (%)82.2%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:41.798055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile808.3
Maximum25387
Range25387
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1334.333715
Coefficient of variation (CV)6.171756315
Kurtosis182.670663
Mean216.2
Median Absolute Deviation (MAD)0
Skewness12.33389702
Sum1058299
Variance1780446.464
MonotonicityNot monotonic
2023-07-21T13:17:41.899523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4112
82.2%
75 8
 
0.2%
150 8
 
0.2%
50 8
 
0.2%
70 8
 
0.2%
60 7
 
0.1%
262 5
 
0.1%
64 5
 
0.1%
149 5
 
0.1%
59 5
 
0.1%
Other values (567) 724
 
14.5%
(Missing) 105
 
2.1%
ValueCountFrequency (%)
0 4112
82.2%
29 1
 
< 0.1%
35 1
 
< 0.1%
38 1
 
< 0.1%
45 1
 
< 0.1%
ValueCountFrequency (%)
25387 1
< 0.1%
25024 1
< 0.1%
24518 1
< 0.1%
23521 1
< 0.1%
22581 1
< 0.1%

tot_cur_bal
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4853
Distinct (%)99.1%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean141812.4787
Minimum61
Maximum1971043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:42.001723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile8307.8
Q129298
median77733
Q3209286.5
95-th percentile447760.7
Maximum1971043
Range1970982
Interquartile range (IQR)179988.5

Descriptive statistics

Standard deviation157726.9767
Coefficient of variation (CV)1.112222127
Kurtosis9.45322779
Mean141812.4787
Median Absolute Deviation (MAD)61797
Skewness2.240738138
Sum694172083
Variance2.487779918 × 1010
MonotonicityNot monotonic
2023-07-21T13:17:42.110281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36853 2
 
< 0.1%
20494 2
 
< 0.1%
20504 2
 
< 0.1%
15546 2
 
< 0.1%
39210 2
 
< 0.1%
198548 2
 
< 0.1%
16429 2
 
< 0.1%
33043 2
 
< 0.1%
24748 2
 
< 0.1%
7359 2
 
< 0.1%
Other values (4843) 4875
97.5%
(Missing) 105
 
2.1%
ValueCountFrequency (%)
61 1
< 0.1%
81 1
< 0.1%
101 1
< 0.1%
172 1
< 0.1%
248 1
< 0.1%
ValueCountFrequency (%)
1971043 1
< 0.1%
1348769 1
< 0.1%
1303280 1
< 0.1%
1301008 1
< 0.1%
1289408 1
< 0.1%

open_acc_6m
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)0.4%
Missing1870
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean0.9364217252
Minimum0
Maximum15
Zeros1399
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:42.201051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.158341008
Coefficient of variation (CV)1.236986474
Kurtosis10.05226255
Mean0.9364217252
Median Absolute Deviation (MAD)1
Skewness2.070895572
Sum2931
Variance1.34175389
MonotonicityNot monotonic
2023-07-21T13:17:42.267242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1399
28.0%
1 999
20.0%
2 439
 
8.8%
3 193
 
3.9%
4 56
 
1.1%
5 30
 
0.6%
6 8
 
0.2%
7 3
 
0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 1870
37.4%
ValueCountFrequency (%)
0 1399
28.0%
1 999
20.0%
2 439
 
8.8%
3 193
 
3.9%
4 56
 
1.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
7 3
 
0.1%
6 8
0.2%

open_act_il
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct24
Distinct (%)0.8%
Missing1870
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean2.632587859
Minimum0
Maximum27
Zeros390
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:42.349210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile8
Maximum27
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.79749443
Coefficient of variation (CV)1.062640481
Kurtosis11.41096419
Mean2.632587859
Median Absolute Deviation (MAD)1
Skewness2.814881328
Sum8240
Variance7.825975084
MonotonicityNot monotonic
2023-07-21T13:17:42.433616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 830
16.6%
2 779
15.6%
3 452
 
9.0%
0 390
 
7.8%
4 227
 
4.5%
5 145
 
2.9%
6 80
 
1.6%
7 43
 
0.9%
8 42
 
0.8%
9 36
 
0.7%
Other values (14) 106
 
2.1%
(Missing) 1870
37.4%
ValueCountFrequency (%)
0 390
7.8%
1 830
16.6%
2 779
15.6%
3 452
9.0%
4 227
 
4.5%
ValueCountFrequency (%)
27 1
 
< 0.1%
22 1
 
< 0.1%
21 3
0.1%
20 4
0.1%
19 2
< 0.1%

open_il_12m
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)0.3%
Missing1870
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean0.66485623
Minimum0
Maximum20
Zeros1736
Zeros (%)34.7%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:42.509951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9797297103
Coefficient of variation (CV)1.473596345
Kurtosis51.31154332
Mean0.66485623
Median Absolute Deviation (MAD)0
Skewness3.911543374
Sum2081
Variance0.9598703053
MonotonicityNot monotonic
2023-07-21T13:17:42.580880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1736
34.7%
1 927
18.5%
2 326
 
6.5%
3 100
 
2.0%
4 26
 
0.5%
5 7
 
0.1%
6 6
 
0.1%
7 1
 
< 0.1%
20 1
 
< 0.1%
(Missing) 1870
37.4%
ValueCountFrequency (%)
0 1736
34.7%
1 927
18.5%
2 326
 
6.5%
3 100
 
2.0%
4 26
 
0.5%
ValueCountFrequency (%)
20 1
 
< 0.1%
7 1
 
< 0.1%
6 6
 
0.1%
5 7
 
0.1%
4 26
0.5%

open_il_24m
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct15
Distinct (%)0.5%
Missing1870
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean1.555910543
Minimum0
Maximum26
Zeros855
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:42.661897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum26
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.629144483
Coefficient of variation (CV)1.04706822
Kurtosis19.99408955
Mean1.555910543
Median Absolute Deviation (MAD)1
Skewness2.59732299
Sum4870
Variance2.654111747
MonotonicityNot monotonic
2023-07-21T13:17:42.738211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 987
19.7%
0 855
17.1%
2 647
 
12.9%
3 329
 
6.6%
4 149
 
3.0%
5 77
 
1.5%
6 43
 
0.9%
7 25
 
0.5%
8 7
 
0.1%
10 4
 
0.1%
Other values (5) 7
 
0.1%
(Missing) 1870
37.4%
ValueCountFrequency (%)
0 855
17.1%
1 987
19.7%
2 647
12.9%
3 329
 
6.6%
4 149
 
3.0%
ValueCountFrequency (%)
26 1
 
< 0.1%
13 1
 
< 0.1%
12 2
< 0.1%
11 1
 
< 0.1%
10 4
0.1%

mths_since_rcnt_il
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct151
Distinct (%)5.0%
Missing1963
Missing (%)39.3%
Infinite0
Infinite (%)0.0%
Mean21.75897267
Minimum0
Maximum414
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:43.151447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median13
Q324
95-th percentile78.2
Maximum414
Range414
Interquartile range (IQR)17

Descriptive statistics

Standard deviation27.02277603
Coefficient of variation (CV)1.241914149
Kurtosis23.16858219
Mean21.75897267
Median Absolute Deviation (MAD)8
Skewness3.644653795
Sum66082
Variance730.2304242
MonotonicityNot monotonic
2023-07-21T13:17:43.241092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 144
 
2.9%
4 128
 
2.6%
2 127
 
2.5%
11 125
 
2.5%
5 122
 
2.4%
3 122
 
2.4%
13 120
 
2.4%
12 118
 
2.4%
10 113
 
2.3%
6 113
 
2.3%
Other values (141) 1805
36.1%
(Missing) 1963
39.3%
ValueCountFrequency (%)
0 3
 
0.1%
1 70
1.4%
2 127
2.5%
3 122
2.4%
4 128
2.6%
ValueCountFrequency (%)
414 1
< 0.1%
207 1
< 0.1%
204 1
< 0.1%
187 1
< 0.1%
173 1
< 0.1%

total_bal_il
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct2705
Distinct (%)86.4%
Missing1870
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean34667.49201
Minimum0
Maximum417152
Zeros372
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:43.339269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18107.5
median22392.5
Q343885.5
95-th percentile110747.65
Maximum417152
Range417152
Interquartile range (IQR)35778

Descriptive statistics

Standard deviation43338.35794
Coefficient of variation (CV)1.250115178
Kurtosis15.84923757
Mean34667.49201
Median Absolute Deviation (MAD)16631
Skewness3.256191082
Sum108509250
Variance1878213269
MonotonicityNot monotonic
2023-07-21T13:17:43.435901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 372
 
7.4%
19418 2
 
< 0.1%
37750 2
 
< 0.1%
4965 2
 
< 0.1%
13777 2
 
< 0.1%
26083 2
 
< 0.1%
35391 2
 
< 0.1%
10869 2
 
< 0.1%
10367 2
 
< 0.1%
27186 2
 
< 0.1%
Other values (2695) 2740
54.8%
(Missing) 1870
37.4%
ValueCountFrequency (%)
0 372
7.4%
44 1
 
< 0.1%
77 1
 
< 0.1%
84 1
 
< 0.1%
108 1
 
< 0.1%
ValueCountFrequency (%)
417152 1
< 0.1%
385589 1
< 0.1%
353584 1
< 0.1%
352471 1
< 0.1%
349581 1
< 0.1%

il_util
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct135
Distinct (%)5.1%
Missing2328
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean68.16654192
Minimum0
Maximum148
Zeros15
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:43.534250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23
Q155
median71
Q384
95-th percentile100
Maximum148
Range148
Interquartile range (IQR)29

Descriptive statistics

Standard deviation23.60244872
Coefficient of variation (CV)0.3462468251
Kurtosis0.3095557765
Mean68.16654192
Median Absolute Deviation (MAD)15
Skewness-0.4808633838
Sum182141
Variance557.0755855
MonotonicityNot monotonic
2023-07-21T13:17:43.634923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 54
 
1.1%
76 53
 
1.1%
77 52
 
1.0%
69 52
 
1.0%
84 52
 
1.0%
82 52
 
1.0%
72 51
 
1.0%
83 50
 
1.0%
80 50
 
1.0%
66 50
 
1.0%
Other values (125) 2156
43.1%
(Missing) 2328
46.6%
ValueCountFrequency (%)
0 15
0.3%
2 2
 
< 0.1%
3 7
0.1%
4 3
 
0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
148 1
< 0.1%
143 1
< 0.1%
138 1
< 0.1%
136 1
< 0.1%
134 1
< 0.1%

open_rv_12m
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct13
Distinct (%)0.4%
Missing1870
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean1.324920128
Minimum0
Maximum14
Zeros1132
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:43.721793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum14
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.543712309
Coefficient of variation (CV)1.165136129
Kurtosis6.527588834
Mean1.324920128
Median Absolute Deviation (MAD)1
Skewness1.959180109
Sum4147
Variance2.383047693
MonotonicityNot monotonic
2023-07-21T13:17:43.788138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 1132
22.6%
1 939
18.8%
2 513
 
10.3%
3 282
 
5.6%
4 135
 
2.7%
5 57
 
1.1%
6 36
 
0.7%
7 20
 
0.4%
8 6
 
0.1%
10 4
 
0.1%
Other values (3) 6
 
0.1%
(Missing) 1870
37.4%
ValueCountFrequency (%)
0 1132
22.6%
1 939
18.8%
2 513
10.3%
3 282
 
5.6%
4 135
 
2.7%
ValueCountFrequency (%)
14 1
 
< 0.1%
13 2
 
< 0.1%
10 4
0.1%
9 3
0.1%
8 6
0.1%

open_rv_24m
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct23
Distinct (%)0.7%
Missing1870
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean2.808945687
Minimum0
Maximum26
Zeros503
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:43.868083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.682385133
Coefficient of variation (CV)0.954943752
Kurtosis7.423921268
Mean2.808945687
Median Absolute Deviation (MAD)1
Skewness2.012658368
Sum8792
Variance7.195190003
MonotonicityNot monotonic
2023-07-21T13:17:43.951338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 655
 
13.1%
2 580
 
11.6%
0 503
 
10.1%
3 460
 
9.2%
4 327
 
6.5%
5 181
 
3.6%
6 158
 
3.2%
7 92
 
1.8%
8 60
 
1.2%
9 34
 
0.7%
Other values (13) 80
 
1.6%
(Missing) 1870
37.4%
ValueCountFrequency (%)
0 503
10.1%
1 655
13.1%
2 580
11.6%
3 460
9.2%
4 327
6.5%
ValueCountFrequency (%)
26 1
 
< 0.1%
24 1
 
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
18 4
0.1%

max_bal_bc
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct2692
Distinct (%)86.0%
Missing1870
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean5855.528754
Minimum0
Maximum133420
Zeros76
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:44.043675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile346.9
Q12197.25
median4231.5
Q37627
95-th percentile16641.05
Maximum133420
Range133420
Interquartile range (IQR)5429.75

Descriptive statistics

Standard deviation6316.49698
Coefficient of variation (CV)1.078723587
Kurtosis79.71972609
Mean5855.528754
Median Absolute Deviation (MAD)2429
Skewness5.793739359
Sum18327805
Variance39898134.1
MonotonicityNot monotonic
2023-07-21T13:17:44.160734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 76
 
1.5%
4000 6
 
0.1%
3000 4
 
0.1%
422 4
 
0.1%
2725 4
 
0.1%
4229 3
 
0.1%
8686 3
 
0.1%
1900 3
 
0.1%
2801 3
 
0.1%
2537 3
 
0.1%
Other values (2682) 3021
60.4%
(Missing) 1870
37.4%
ValueCountFrequency (%)
0 76
1.5%
1 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
133420 1
< 0.1%
99609 1
< 0.1%
80662 1
< 0.1%
63916 1
< 0.1%
46991 1
< 0.1%

all_util
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)3.7%
Missing1871
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean56.51198466
Minimum0
Maximum124
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:44.262546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q142
median58
Q371
95-th percentile89
Maximum124
Range124
Interquartile range (IQR)29

Descriptive statistics

Standard deviation20.88743887
Coefficient of variation (CV)0.3696107825
Kurtosis-0.2809132343
Mean56.51198466
Median Absolute Deviation (MAD)14
Skewness-0.2116985596
Sum176826
Variance436.2851025
MonotonicityNot monotonic
2023-07-21T13:17:44.365465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 79
 
1.6%
64 66
 
1.3%
51 66
 
1.3%
63 66
 
1.3%
61 65
 
1.3%
62 64
 
1.3%
69 61
 
1.2%
72 60
 
1.2%
53 59
 
1.2%
59 58
 
1.2%
Other values (105) 2485
49.7%
(Missing) 1871
37.4%
ValueCountFrequency (%)
0 6
0.1%
1 3
0.1%
2 3
0.1%
3 4
0.1%
4 4
0.1%
ValueCountFrequency (%)
124 1
 
< 0.1%
118 1
 
< 0.1%
116 1
 
< 0.1%
115 1
 
< 0.1%
114 5
0.1%

total_rev_hi_lim
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1145
Distinct (%)23.4%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean35156.96568
Minimum0
Maximum500000
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:44.465507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5900
Q114800
median25900
Q343700
95-th percentile91880
Maximum500000
Range500000
Interquartile range (IQR)28900

Descriptive statistics

Standard deviation34863.6722
Coefficient of variation (CV)0.9916575998
Kurtosis26.83704656
Mean35156.96568
Median Absolute Deviation (MAD)13000
Skewness3.9152839
Sum172093347
Variance1215475639
MonotonicityNot monotonic
2023-07-21T13:17:44.568014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19900 20
 
0.4%
24800 20
 
0.4%
14800 20
 
0.4%
10500 18
 
0.4%
13300 18
 
0.4%
5800 18
 
0.4%
9000 18
 
0.4%
18500 18
 
0.4%
26500 17
 
0.3%
12300 17
 
0.3%
Other values (1135) 4711
94.2%
(Missing) 105
 
2.1%
ValueCountFrequency (%)
0 3
0.1%
500 2
< 0.1%
800 1
 
< 0.1%
900 2
< 0.1%
1000 1
 
< 0.1%
ValueCountFrequency (%)
500000 1
< 0.1%
416900 1
< 0.1%
412800 1
< 0.1%
382000 1
< 0.1%
370000 1
< 0.1%

inq_fi
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct15
Distinct (%)0.5%
Missing1870
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean1.054632588
Minimum0
Maximum19
Zeros1542
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:44.652493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.544447247
Coefficient of variation (CV)1.464441043
Kurtosis15.22424155
Mean1.054632588
Median Absolute Deviation (MAD)1
Skewness2.80604212
Sum3301
Variance2.385317299
MonotonicityNot monotonic
2023-07-21T13:17:44.722870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 1542
30.8%
1 774
15.5%
2 400
 
8.0%
3 196
 
3.9%
4 103
 
2.1%
5 55
 
1.1%
6 27
 
0.5%
7 14
 
0.3%
8 9
 
0.2%
10 3
 
0.1%
Other values (5) 7
 
0.1%
(Missing) 1870
37.4%
ValueCountFrequency (%)
0 1542
30.8%
1 774
15.5%
2 400
 
8.0%
3 196
 
3.9%
4 103
 
2.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
17 1
 
< 0.1%
14 1
 
< 0.1%
11 2
< 0.1%
10 3
0.1%

total_cu_tl
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct21
Distinct (%)0.7%
Missing1870
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean1.460063898
Minimum0
Maximum24
Zeros1673
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:44.800794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile7
Maximum24
Range24
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.539021636
Coefficient of variation (CV)1.738979808
Kurtosis10.99637898
Mean1.460063898
Median Absolute Deviation (MAD)0
Skewness2.859038729
Sum4570
Variance6.446630868
MonotonicityNot monotonic
2023-07-21T13:17:44.881663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 1673
33.5%
1 552
 
11.0%
2 300
 
6.0%
3 160
 
3.2%
4 124
 
2.5%
5 98
 
2.0%
6 61
 
1.2%
7 39
 
0.8%
8 33
 
0.7%
9 22
 
0.4%
Other values (11) 68
 
1.4%
(Missing) 1870
37.4%
ValueCountFrequency (%)
0 1673
33.5%
1 552
 
11.0%
2 300
 
6.0%
3 160
 
3.2%
4 124
 
2.5%
ValueCountFrequency (%)
24 1
< 0.1%
23 1
< 0.1%
19 1
< 0.1%
17 2
< 0.1%
16 1
< 0.1%

inq_last_12m
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct21
Distinct (%)0.7%
Missing1870
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean2.061661342
Minimum0
Maximum31
Zeros871
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:44.961854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile7
Maximum31
Range31
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.411693798
Coefficient of variation (CV)1.169781743
Kurtosis13.48948617
Mean2.061661342
Median Absolute Deviation (MAD)1
Skewness2.576858143
Sum6453
Variance5.816266974
MonotonicityNot monotonic
2023-07-21T13:17:45.036242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 871
17.4%
1 773
15.5%
2 542
 
10.8%
3 346
 
6.9%
4 218
 
4.4%
5 123
 
2.5%
6 87
 
1.7%
7 62
 
1.2%
9 30
 
0.6%
8 30
 
0.6%
Other values (11) 48
 
1.0%
(Missing) 1870
37.4%
ValueCountFrequency (%)
0 871
17.4%
1 773
15.5%
2 542
10.8%
3 346
 
6.9%
4 218
 
4.4%
ValueCountFrequency (%)
31 1
< 0.1%
21 2
< 0.1%
20 1
< 0.1%
19 1
< 0.1%
17 1
< 0.1%

acc_open_past_24mths
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)0.5%
Missing75
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean4.523654822
Minimum0
Maximum40
Zeros227
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:45.130225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum40
Range40
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.230153871
Coefficient of variation (CV)0.7140584324
Kurtosis6.042675833
Mean4.523654822
Median Absolute Deviation (MAD)2
Skewness1.576481592
Sum22279
Variance10.43389403
MonotonicityNot monotonic
2023-07-21T13:17:45.230255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3 758
15.2%
2 689
13.8%
4 667
13.3%
5 552
11.0%
1 500
10.0%
6 461
9.2%
7 325
6.5%
8 232
 
4.6%
0 227
 
4.5%
9 157
 
3.1%
Other values (15) 357
7.1%
ValueCountFrequency (%)
0 227
 
4.5%
1 500
10.0%
2 689
13.8%
3 758
15.2%
4 667
13.3%
ValueCountFrequency (%)
40 1
 
< 0.1%
28 1
 
< 0.1%
24 2
 
< 0.1%
22 5
0.1%
20 3
0.1%

avg_cur_bal
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4436
Distinct (%)90.6%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean13551.95444
Minimum16
Maximum281578
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:45.346904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile1098.1
Q13085.5
median7313
Q318446
95-th percentile43827.4
Maximum281578
Range281562
Interquartile range (IQR)15360.5

Descriptive statistics

Standard deviation16401.84706
Coefficient of variation (CV)1.210293845
Kurtosis27.43902417
Mean13551.95444
Median Absolute Deviation (MAD)5333
Skewness3.535763278
Sum66336817
Variance269020586.8
MonotonicityNot monotonic
2023-07-21T13:17:45.460459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2662 4
 
0.1%
1801 3
 
0.1%
3099 3
 
0.1%
4990 3
 
0.1%
2507 3
 
0.1%
13741 3
 
0.1%
1751 3
 
0.1%
2421 3
 
0.1%
1307 3
 
0.1%
1190 3
 
0.1%
Other values (4426) 4864
97.3%
(Missing) 105
 
2.1%
ValueCountFrequency (%)
16 1
< 0.1%
25 1
< 0.1%
61 2
< 0.1%
83 1
< 0.1%
86 1
< 0.1%
ValueCountFrequency (%)
281578 1
< 0.1%
184626 1
< 0.1%
180805 1
< 0.1%
168596 1
< 0.1%
149834 1
< 0.1%

bc_open_to_buy
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct4161
Distinct (%)85.3%
Missing123
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean11486.79188
Minimum0
Maximum214540
Zeros76
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:45.580931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile161.6
Q11692
median5485
Q314567
95-th percentile43056.6
Maximum214540
Range214540
Interquartile range (IQR)12875

Descriptive statistics

Standard deviation16455.26606
Coefficient of variation (CV)1.432538017
Kurtosis21.83314661
Mean11486.79188
Median Absolute Deviation (MAD)4679
Skewness3.575337344
Sum56021084
Variance270775781.1
MonotonicityNot monotonic
2023-07-21T13:17:45.703882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 76
 
1.5%
4500 6
 
0.1%
1000 5
 
0.1%
66 5
 
0.1%
457 4
 
0.1%
11500 4
 
0.1%
2932 4
 
0.1%
661 4
 
0.1%
2076 4
 
0.1%
429 4
 
0.1%
Other values (4151) 4761
95.2%
(Missing) 123
 
2.5%
ValueCountFrequency (%)
0 76
1.5%
1 2
 
< 0.1%
7 1
 
< 0.1%
12 1
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
214540 1
< 0.1%
204109 1
< 0.1%
192570 1
< 0.1%
163630 1
< 0.1%
142221 1
< 0.1%

bc_util
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1021
Distinct (%)20.9%
Missing124
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean57.89411403
Minimum0
Maximum123.3
Zeros60
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:45.798735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.8
Q135.075
median60.45
Q383.4
95-th percentile97.8
Maximum123.3
Range123.3
Interquartile range (IQR)48.325

Descriptive statistics

Standard deviation28.61072785
Coefficient of variation (CV)0.4941906155
Kurtosis-1.035681232
Mean57.89411403
Median Absolute Deviation (MAD)23.95
Skewness-0.2714628396
Sum282291.7
Variance818.5737479
MonotonicityNot monotonic
2023-07-21T13:17:45.904333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60
 
1.2%
98 22
 
0.4%
95.1 17
 
0.3%
99 17
 
0.3%
21.2 17
 
0.3%
96 17
 
0.3%
96.4 16
 
0.3%
63.4 14
 
0.3%
92 14
 
0.3%
96.8 14
 
0.3%
Other values (1011) 4668
93.4%
(Missing) 124
 
2.5%
ValueCountFrequency (%)
0 60
1.2%
0.1 3
 
0.1%
0.2 3
 
0.1%
0.3 6
 
0.1%
0.4 3
 
0.1%
ValueCountFrequency (%)
123.3 1
< 0.1%
112.5 1
< 0.1%
110.1 1
< 0.1%
105.4 1
< 0.1%
104.7 1
< 0.1%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
0.0
4953 
1.0
 
42
2.0
 
3
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15000
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4953
99.1%
1.0 42
 
0.8%
2.0 3
 
0.1%
3.0 2
 
< 0.1%

Length

2023-07-21T13:17:45.997245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:46.075034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4953
99.1%
1.0 42
 
0.8%
2.0 3
 
0.1%
3.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 9953
66.4%
. 5000
33.3%
1 42
 
0.3%
2 3
 
< 0.1%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
66.7%
Other Punctuation 5000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9953
99.5%
1 42
 
0.4%
2 3
 
< 0.1%
3 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9953
66.4%
. 5000
33.3%
1 42
 
0.3%
2 3
 
< 0.1%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9953
66.4%
. 5000
33.3%
1 42
 
0.3%
2 3
 
< 0.1%
3 2
 
< 0.1%

delinq_amnt
Real number (ℝ)

SKEWED  ZEROS 

Distinct16
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6254
Minimum0
Maximum9185
Zeros4985
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:46.139826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9185
Range9185
Interquartile range (IQR)0

Descriptive statistics

Standard deviation219.4903986
Coefficient of variation (CV)33.12862599
Kurtosis1325.539991
Mean6.6254
Median Absolute Deviation (MAD)0
Skewness36.04017216
Sum33127
Variance48176.03508
MonotonicityNot monotonic
2023-07-21T13:17:46.212790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 4985
99.7%
472 1
 
< 0.1%
966 1
 
< 0.1%
156 1
 
< 0.1%
159 1
 
< 0.1%
9185 1
 
< 0.1%
65 1
 
< 0.1%
24 1
 
< 0.1%
205 1
 
< 0.1%
30 1
 
< 0.1%
Other values (6) 6
 
0.1%
ValueCountFrequency (%)
0 4985
99.7%
24 1
 
< 0.1%
30 1
 
< 0.1%
50 1
 
< 0.1%
65 1
 
< 0.1%
ValueCountFrequency (%)
9185 1
< 0.1%
7669 1
< 0.1%
7645 1
< 0.1%
6174 1
< 0.1%
966 1
< 0.1%

mo_sin_old_il_acct
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct309
Distinct (%)6.5%
Missing249
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean124.6228162
Minimum1
Maximum540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:46.300328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile32
Q195
median129
Q3153
95-th percentile213
Maximum540
Range539
Interquartile range (IQR)58

Descriptive statistics

Standard deviation53.48486473
Coefficient of variation (CV)0.4291739373
Kurtosis2.297200573
Mean124.6228162
Median Absolute Deviation (MAD)27
Skewness0.4519019202
Sum592083
Variance2860.630755
MonotonicityNot monotonic
2023-07-21T13:17:46.408856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127 65
 
1.3%
128 65
 
1.3%
126 61
 
1.2%
123 60
 
1.2%
138 60
 
1.2%
129 60
 
1.2%
132 60
 
1.2%
125 59
 
1.2%
141 59
 
1.2%
140 58
 
1.2%
Other values (299) 4144
82.9%
(Missing) 249
 
5.0%
ValueCountFrequency (%)
1 3
0.1%
2 2
< 0.1%
3 3
0.1%
4 1
 
< 0.1%
5 4
0.1%
ValueCountFrequency (%)
540 1
< 0.1%
434 1
< 0.1%
401 1
< 0.1%
399 1
< 0.1%
389 1
< 0.1%

mo_sin_old_rev_tl_op
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct479
Distinct (%)9.8%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean182.2506639
Minimum12
Maximum821
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:46.514199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile49
Q1118
median165
Q3233
95-th percentile368
Maximum821
Range809
Interquartile range (IQR)115

Descriptive statistics

Standard deviation96.75162678
Coefficient of variation (CV)0.5308711897
Kurtosis1.602283907
Mean182.2506639
Median Absolute Deviation (MAD)55
Skewness1.019586573
Sum892117
Variance9360.877286
MonotonicityNot monotonic
2023-07-21T13:17:46.613673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147 39
 
0.8%
124 34
 
0.7%
125 34
 
0.7%
158 34
 
0.7%
133 33
 
0.7%
138 33
 
0.7%
162 33
 
0.7%
169 32
 
0.6%
145 32
 
0.6%
139 31
 
0.6%
Other values (469) 4560
91.2%
(Missing) 105
 
2.1%
ValueCountFrequency (%)
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 2
< 0.1%
16 2
< 0.1%
ValueCountFrequency (%)
821 1
< 0.1%
682 1
< 0.1%
651 1
< 0.1%
645 1
< 0.1%
636 1
< 0.1%

mo_sin_rcnt_rev_tl_op
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct125
Distinct (%)2.6%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean14.14525026
Minimum0
Maximum279
Zeros75
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:46.720408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median8
Q317
95-th percentile46
Maximum279
Range279
Interquartile range (IQR)13

Descriptive statistics

Standard deviation18.32457069
Coefficient of variation (CV)1.295457511
Kurtosis28.17598991
Mean14.14525026
Median Absolute Deviation (MAD)5
Skewness4.07072991
Sum69241
Variance335.7898911
MonotonicityNot monotonic
2023-07-21T13:17:46.811476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 382
 
7.6%
3 361
 
7.2%
4 319
 
6.4%
1 302
 
6.0%
5 295
 
5.9%
6 271
 
5.4%
7 241
 
4.8%
8 224
 
4.5%
10 201
 
4.0%
9 170
 
3.4%
Other values (115) 2129
42.6%
ValueCountFrequency (%)
0 75
 
1.5%
1 302
6.0%
2 382
7.6%
3 361
7.2%
4 319
6.4%
ValueCountFrequency (%)
279 1
< 0.1%
223 1
< 0.1%
199 1
< 0.1%
193 1
< 0.1%
190 1
< 0.1%

mo_sin_rcnt_tl
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct79
Distinct (%)1.6%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean8.3546476
Minimum0
Maximum126
Zeros78
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:47.230576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q311
95-th percentile24
Maximum126
Range126
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.306021136
Coefficient of variation (CV)1.113873569
Kurtosis32.14065808
Mean8.3546476
Median Absolute Deviation (MAD)4
Skewness4.279159142
Sum40896
Variance86.60202938
MonotonicityNot monotonic
2023-07-21T13:17:47.330802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 542
10.8%
3 505
 
10.1%
4 435
 
8.7%
1 407
 
8.1%
5 388
 
7.8%
6 353
 
7.1%
7 290
 
5.8%
8 252
 
5.0%
9 202
 
4.0%
10 193
 
3.9%
Other values (69) 1328
26.6%
ValueCountFrequency (%)
0 78
 
1.6%
1 407
8.1%
2 542
10.8%
3 505
10.1%
4 435
8.7%
ValueCountFrequency (%)
126 1
< 0.1%
113 1
< 0.1%
110 1
< 0.1%
108 1
< 0.1%
107 1
< 0.1%

mort_acc
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct16
Distinct (%)0.3%
Missing75
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean1.557563452
Minimum0
Maximum16
Zeros2081
Zeros (%)41.6%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:47.416394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8952531
Coefficient of variation (CV)1.216806351
Kurtosis3.11936416
Mean1.557563452
Median Absolute Deviation (MAD)1
Skewness1.5246564
Sum7671
Variance3.591984314
MonotonicityNot monotonic
2023-07-21T13:17:47.494596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 2081
41.6%
1 862
17.2%
2 715
 
14.3%
3 531
 
10.6%
4 327
 
6.5%
5 186
 
3.7%
6 117
 
2.3%
7 60
 
1.2%
8 23
 
0.5%
9 8
 
0.2%
Other values (6) 15
 
0.3%
(Missing) 75
 
1.5%
ValueCountFrequency (%)
0 2081
41.6%
1 862
17.2%
2 715
 
14.3%
3 531
 
10.6%
4 327
 
6.5%
ValueCountFrequency (%)
16 1
< 0.1%
15 1
< 0.1%
13 1
< 0.1%
12 2
< 0.1%
11 2
< 0.1%

mths_since_recent_bc
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct200
Distinct (%)4.1%
Missing116
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean25.36404586
Minimum0
Maximum341
Zeros35
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:47.621716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median14
Q330
95-th percentile91
Maximum341
Range341
Interquartile range (IQR)24

Descriptive statistics

Standard deviation33.96251661
Coefficient of variation (CV)1.339002334
Kurtosis17.0483247
Mean25.36404586
Median Absolute Deviation (MAD)9
Skewness3.440978649
Sum123878
Variance1153.452535
MonotonicityNot monotonic
2023-07-21T13:17:47.728392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 249
 
5.0%
2 225
 
4.5%
4 225
 
4.5%
5 213
 
4.3%
6 197
 
3.9%
7 192
 
3.8%
8 180
 
3.6%
10 174
 
3.5%
12 153
 
3.1%
9 153
 
3.1%
Other values (190) 2923
58.5%
ValueCountFrequency (%)
0 35
 
0.7%
1 149
3.0%
2 225
4.5%
3 249
5.0%
4 225
4.5%
ValueCountFrequency (%)
341 1
< 0.1%
322 1
< 0.1%
316 1
< 0.1%
307 1
< 0.1%
304 1
< 0.1%

mths_since_recent_bc_dlq
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct97
Distinct (%)8.5%
Missing3854
Missing (%)77.1%
Infinite0
Infinite (%)0.0%
Mean39.40663176
Minimum0
Maximum118
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:47.831212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.25
Q120
median37
Q359
95-th percentile76
Maximum118
Range118
Interquartile range (IQR)39

Descriptive statistics

Standard deviation22.65373504
Coefficient of variation (CV)0.5748711329
Kurtosis-0.8640214905
Mean39.40663176
Median Absolute Deviation (MAD)19
Skewness0.2714395792
Sum45160
Variance513.1917114
MonotonicityNot monotonic
2023-07-21T13:17:47.932248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 26
 
0.5%
15 24
 
0.5%
34 23
 
0.5%
28 22
 
0.4%
29 22
 
0.4%
13 22
 
0.4%
23 21
 
0.4%
33 21
 
0.4%
16 21
 
0.4%
67 21
 
0.4%
Other values (87) 923
 
18.5%
(Missing) 3854
77.1%
ValueCountFrequency (%)
0 4
 
0.1%
1 8
0.2%
2 6
0.1%
3 10
0.2%
4 9
0.2%
ValueCountFrequency (%)
118 1
< 0.1%
100 1
< 0.1%
99 1
< 0.1%
98 1
< 0.1%
94 1
< 0.1%

mths_since_recent_inq
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)0.6%
Missing612
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean7.019143118
Minimum0
Maximum24
Zeros338
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:48.024268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q311
95-th percentile19
Maximum24
Range24
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.957305587
Coefficient of variation (CV)0.8487226271
Kurtosis-0.08218000145
Mean7.019143118
Median Absolute Deviation (MAD)4
Skewness0.883160851
Sum30800
Variance35.48948985
MonotonicityNot monotonic
2023-07-21T13:17:48.108253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 508
 
10.2%
2 416
 
8.3%
3 355
 
7.1%
0 338
 
6.8%
4 322
 
6.4%
6 269
 
5.4%
5 254
 
5.1%
7 232
 
4.6%
8 221
 
4.4%
9 190
 
3.8%
Other values (15) 1283
25.7%
(Missing) 612
12.2%
ValueCountFrequency (%)
0 338
6.8%
1 508
10.2%
2 416
8.3%
3 355
7.1%
4 322
6.4%
ValueCountFrequency (%)
24 18
 
0.4%
23 37
0.7%
22 55
1.1%
21 37
0.7%
20 52
1.0%

mths_since_recent_revol_delinq
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct96
Distinct (%)5.8%
Missing3358
Missing (%)67.2%
Infinite0
Infinite (%)0.0%
Mean35.59439708
Minimum0
Maximum113
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:48.207433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q117
median32
Q353
95-th percentile75
Maximum113
Range113
Interquartile range (IQR)36

Descriptive statistics

Standard deviation22.31490142
Coefficient of variation (CV)0.626921742
Kurtosis-0.7367276958
Mean35.59439708
Median Absolute Deviation (MAD)17
Skewness0.4607995153
Sum58446
Variance497.9548254
MonotonicityNot monotonic
2023-07-21T13:17:48.302995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 39
 
0.8%
15 38
 
0.8%
31 35
 
0.7%
9 34
 
0.7%
22 34
 
0.7%
28 31
 
0.6%
29 30
 
0.6%
12 29
 
0.6%
6 28
 
0.6%
16 28
 
0.6%
Other values (86) 1316
 
26.3%
(Missing) 3358
67.2%
ValueCountFrequency (%)
0 5
 
0.1%
1 11
0.2%
2 14
0.3%
3 14
0.3%
4 22
0.4%
ValueCountFrequency (%)
113 1
< 0.1%
100 1
< 0.1%
98 1
< 0.1%
97 1
< 0.1%
94 2
< 0.1%

num_accts_ever_120_pd
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct16
Distinct (%)0.3%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean0.4870275792
Minimum0
Maximum22
Zeros3739
Zeros (%)74.8%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:48.389746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum22
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.284509439
Coefficient of variation (CV)2.637447022
Kurtosis48.59157206
Mean0.4870275792
Median Absolute Deviation (MAD)0
Skewness5.365543477
Sum2384
Variance1.649964498
MonotonicityNot monotonic
2023-07-21T13:17:48.461859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 3739
74.8%
1 649
 
13.0%
2 246
 
4.9%
3 107
 
2.1%
4 49
 
1.0%
5 40
 
0.8%
6 19
 
0.4%
7 18
 
0.4%
8 11
 
0.2%
9 7
 
0.1%
Other values (6) 10
 
0.2%
(Missing) 105
 
2.1%
ValueCountFrequency (%)
0 3739
74.8%
1 649
 
13.0%
2 246
 
4.9%
3 107
 
2.1%
4 49
 
1.0%
ValueCountFrequency (%)
22 1
< 0.1%
20 1
< 0.1%
18 1
< 0.1%
13 1
< 0.1%
11 2
< 0.1%

num_actv_bc_tl
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)0.4%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean3.741573034
Minimum0
Maximum20
Zeros102
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:48.543848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.331448871
Coefficient of variation (CV)0.6231199686
Kurtosis3.466426363
Mean3.741573034
Median Absolute Deviation (MAD)1
Skewness1.390887715
Sum18315
Variance5.435653839
MonotonicityNot monotonic
2023-07-21T13:17:48.641140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 1008
20.2%
2 962
19.2%
4 845
16.9%
1 546
10.9%
5 538
10.8%
6 358
 
7.2%
7 216
 
4.3%
8 119
 
2.4%
0 102
 
2.0%
9 75
 
1.5%
Other values (10) 126
 
2.5%
(Missing) 105
 
2.1%
ValueCountFrequency (%)
0 102
 
2.0%
1 546
10.9%
2 962
19.2%
3 1008
20.2%
4 845
16.9%
ValueCountFrequency (%)
20 1
 
< 0.1%
18 2
 
< 0.1%
17 1
 
< 0.1%
16 3
0.1%
15 5
0.1%

num_actv_rev_tl
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)0.6%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean5.686006129
Minimum0
Maximum28
Zeros26
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:48.750340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q37
95-th percentile12
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.362950844
Coefficient of variation (CV)0.5914434082
Kurtosis3.82050654
Mean5.686006129
Median Absolute Deviation (MAD)2
Skewness1.487428103
Sum27833
Variance11.30943838
MonotonicityNot monotonic
2023-07-21T13:17:48.829633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5 760
15.2%
4 713
14.3%
3 681
13.6%
6 513
10.3%
2 458
9.2%
7 437
8.7%
8 339
6.8%
9 242
 
4.8%
1 169
 
3.4%
10 156
 
3.1%
Other values (17) 427
8.5%
ValueCountFrequency (%)
0 26
 
0.5%
1 169
 
3.4%
2 458
9.2%
3 681
13.6%
4 713
14.3%
ValueCountFrequency (%)
28 2
< 0.1%
26 2
< 0.1%
24 4
0.1%
23 1
 
< 0.1%
22 2
< 0.1%

num_bc_sats
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)0.5%
Missing89
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean4.843616371
Minimum0
Maximum29
Zeros40
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:48.918888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile10
Maximum29
Range29
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.013543677
Coefficient of variation (CV)0.622168117
Kurtosis4.47047093
Mean4.843616371
Median Absolute Deviation (MAD)2
Skewness1.555478815
Sum23787
Variance9.081445494
MonotonicityNot monotonic
2023-07-21T13:17:48.997169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4 820
16.4%
3 804
16.1%
5 661
13.2%
2 639
12.8%
6 507
10.1%
7 344
6.9%
1 342
6.8%
8 236
 
4.7%
9 165
 
3.3%
10 115
 
2.3%
Other values (17) 278
 
5.6%
(Missing) 89
 
1.8%
ValueCountFrequency (%)
0 40
 
0.8%
1 342
6.8%
2 639
12.8%
3 804
16.1%
4 820
16.4%
ValueCountFrequency (%)
29 1
< 0.1%
26 1
< 0.1%
24 1
< 0.1%
23 1
< 0.1%
22 2
< 0.1%

num_bc_tl
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)0.7%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean7.715628192
Minimum0
Maximum39
Zeros9
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:49.088609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median7
Q310
95-th percentile17
Maximum39
Range39
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.607392526
Coefficient of variation (CV)0.5971506676
Kurtosis2.841031939
Mean7.715628192
Median Absolute Deviation (MAD)3
Skewness1.333664938
Sum37768
Variance21.22806589
MonotonicityNot monotonic
2023-07-21T13:17:49.185143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
5 516
10.3%
6 514
10.3%
4 505
10.1%
8 447
8.9%
7 438
8.8%
3 384
7.7%
9 344
 
6.9%
10 283
 
5.7%
2 259
 
5.2%
11 255
 
5.1%
Other values (26) 950
19.0%
ValueCountFrequency (%)
0 9
 
0.2%
1 111
 
2.2%
2 259
5.2%
3 384
7.7%
4 505
10.1%
ValueCountFrequency (%)
39 1
< 0.1%
35 1
< 0.1%
33 1
< 0.1%
32 1
< 0.1%
31 1
< 0.1%

num_il_tl
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct53
Distinct (%)1.1%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean8.224923391
Minimum0
Maximum58
Zeros144
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:49.294025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q311
95-th percentile22.3
Maximum58
Range58
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.135809065
Coefficient of variation (CV)0.867583651
Kurtosis5.46678715
Mean8.224923391
Median Absolute Deviation (MAD)3
Skewness1.921117438
Sum40261
Variance50.91977101
MonotonicityNot monotonic
2023-07-21T13:17:49.390175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 455
 
9.1%
5 417
 
8.3%
4 410
 
8.2%
2 393
 
7.9%
6 352
 
7.0%
7 342
 
6.8%
1 324
 
6.5%
8 292
 
5.8%
9 269
 
5.4%
10 223
 
4.5%
Other values (43) 1418
28.4%
ValueCountFrequency (%)
0 144
 
2.9%
1 324
6.5%
2 393
7.9%
3 455
9.1%
4 410
8.2%
ValueCountFrequency (%)
58 1
< 0.1%
57 1
< 0.1%
55 2
< 0.1%
52 1
< 0.1%
49 2
< 0.1%

num_op_rev_tl
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)0.8%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean8.319305414
Minimum0
Maximum54
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:49.485803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median7
Q311
95-th percentile17
Maximum54
Range54
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.699857958
Coefficient of variation (CV)0.5649339367
Kurtosis4.861678661
Mean8.319305414
Median Absolute Deviation (MAD)3
Skewness1.550165561
Sum40723
Variance22.08866482
MonotonicityNot monotonic
2023-07-21T13:17:49.576789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
6 535
10.7%
5 502
10.0%
7 490
9.8%
8 475
9.5%
4 435
8.7%
9 385
 
7.7%
3 309
 
6.2%
10 296
 
5.9%
11 260
 
5.2%
12 227
 
4.5%
Other values (27) 981
19.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 48
 
1.0%
2 165
 
3.3%
3 309
6.2%
4 435
8.7%
ValueCountFrequency (%)
54 1
 
< 0.1%
37 1
 
< 0.1%
36 1
 
< 0.1%
33 2
< 0.1%
32 3
0.1%

num_rev_accts
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct57
Distinct (%)1.2%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean13.96302349
Minimum2
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:49.673727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median12
Q318
95-th percentile29
Maximum68
Range66
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.954461564
Coefficient of variation (CV)0.5696804541
Kurtosis3.404166568
Mean13.96302349
Median Absolute Deviation (MAD)5
Skewness1.397557147
Sum68349
Variance63.27345878
MonotonicityNot monotonic
2023-07-21T13:17:49.772528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 324
 
6.5%
11 298
 
6.0%
7 294
 
5.9%
9 292
 
5.8%
12 281
 
5.6%
8 266
 
5.3%
14 253
 
5.1%
13 244
 
4.9%
6 242
 
4.8%
15 216
 
4.3%
Other values (47) 2185
43.7%
ValueCountFrequency (%)
2 46
 
0.9%
3 88
 
1.8%
4 156
3.1%
5 198
4.0%
6 242
4.8%
ValueCountFrequency (%)
68 1
< 0.1%
65 2
< 0.1%
64 1
< 0.1%
58 1
< 0.1%
55 1
< 0.1%

num_rev_tl_bal_gt_0
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)0.5%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean5.642492339
Minimum0
Maximum26
Zeros25
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:49.869577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q37
95-th percentile12
Maximum26
Range26
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.27659536
Coefficient of variation (CV)0.5807000104
Kurtosis3.096241866
Mean5.642492339
Median Absolute Deviation (MAD)2
Skewness1.375262654
Sum27620
Variance10.73607715
MonotonicityNot monotonic
2023-07-21T13:17:49.956622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
5 764
15.3%
4 715
14.3%
3 681
13.6%
6 523
10.5%
2 456
9.1%
7 434
8.7%
8 354
7.1%
9 230
 
4.6%
1 173
 
3.5%
10 156
 
3.1%
Other values (16) 409
8.2%
ValueCountFrequency (%)
0 25
 
0.5%
1 173
 
3.5%
2 456
9.1%
3 681
13.6%
4 715
14.3%
ValueCountFrequency (%)
26 1
 
< 0.1%
24 3
0.1%
23 1
 
< 0.1%
22 3
0.1%
21 3
0.1%

num_sats
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)0.9%
Missing89
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean11.59254734
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:50.051395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q18
median11
Q314
95-th percentile22
Maximum56
Range55
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.631915296
Coefficient of variation (CV)0.4858220657
Kurtosis3.168353279
Mean11.59254734
Median Absolute Deviation (MAD)3
Skewness1.314436207
Sum56931
Variance31.7184699
MonotonicityNot monotonic
2023-07-21T13:17:50.143174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
9 446
 
8.9%
8 439
 
8.8%
10 403
 
8.1%
11 374
 
7.5%
7 372
 
7.4%
12 321
 
6.4%
6 301
 
6.0%
13 298
 
6.0%
14 275
 
5.5%
5 234
 
4.7%
Other values (34) 1448
29.0%
ValueCountFrequency (%)
1 5
 
0.1%
2 15
 
0.3%
3 72
 
1.4%
4 157
3.1%
5 234
4.7%
ValueCountFrequency (%)
56 1
< 0.1%
47 1
< 0.1%
45 1
< 0.1%
43 1
< 0.1%
42 1
< 0.1%

num_tl_120dpd_2m
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing281
Missing (%)5.6%
Memory size78.1 KiB
0.0
4717 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14157
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4717
94.3%
1.0 2
 
< 0.1%
(Missing) 281
 
5.6%

Length

2023-07-21T13:17:50.228924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:50.308857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4717
> 99.9%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 9436
66.7%
. 4719
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9438
66.7%
Other Punctuation 4719
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9436
> 99.9%
1 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 4719
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14157
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9436
66.7%
. 4719
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14157
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9436
66.7%
. 4719
33.3%
1 2
 
< 0.1%

num_tl_30dpd
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing105
Missing (%)2.1%
Memory size78.1 KiB
0.0
4882 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14685
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4882
97.6%
1.0 13
 
0.3%
(Missing) 105
 
2.1%

Length

2023-07-21T13:17:50.370581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:50.445451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4882
99.7%
1.0 13
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 9777
66.6%
. 4895
33.3%
1 13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9790
66.7%
Other Punctuation 4895
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9777
99.9%
1 13
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 4895
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14685
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9777
66.6%
. 4895
33.3%
1 13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14685
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9777
66.6%
. 4895
33.3%
1 13
 
0.1%

num_tl_90g_dpd_24m
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)0.2%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean0.08232890705
Minimum0
Maximum10
Zeros4631
Zeros (%)92.6%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:50.503425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4697253435
Coefficient of variation (CV)5.705472845
Kurtosis175.9977998
Mean0.08232890705
Median Absolute Deviation (MAD)0
Skewness11.17441788
Sum403
Variance0.2206418983
MonotonicityNot monotonic
2023-07-21T13:17:50.572659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 4631
92.6%
1 200
 
4.0%
2 38
 
0.8%
3 12
 
0.2%
6 4
 
0.1%
4 3
 
0.1%
5 2
 
< 0.1%
9 2
 
< 0.1%
10 2
 
< 0.1%
7 1
 
< 0.1%
(Missing) 105
 
2.1%
ValueCountFrequency (%)
0 4631
92.6%
1 200
 
4.0%
2 38
 
0.8%
3 12
 
0.2%
4 3
 
0.1%
ValueCountFrequency (%)
10 2
< 0.1%
9 2
< 0.1%
7 1
 
< 0.1%
6 4
0.1%
5 2
< 0.1%

num_tl_op_past_12m
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)0.3%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean2.077425945
Minimum0
Maximum28
Zeros952
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:50.648035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum28
Range28
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.879255453
Coefficient of variation (CV)0.9046076743
Kurtosis10.20474586
Mean2.077425945
Median Absolute Deviation (MAD)1
Skewness1.849627982
Sum10169
Variance3.531601056
MonotonicityNot monotonic
2023-07-21T13:17:50.712668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 1261
25.2%
2 1044
20.9%
0 952
19.0%
3 754
15.1%
4 409
 
8.2%
5 232
 
4.6%
6 116
 
2.3%
7 60
 
1.2%
8 23
 
0.5%
9 19
 
0.4%
Other values (7) 25
 
0.5%
(Missing) 105
 
2.1%
ValueCountFrequency (%)
0 952
19.0%
1 1261
25.2%
2 1044
20.9%
3 754
15.1%
4 409
 
8.2%
ValueCountFrequency (%)
28 1
< 0.1%
15 1
< 0.1%
14 1
< 0.1%
13 2
< 0.1%
12 2
< 0.1%

pct_tl_nvr_dlq
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct244
Distinct (%)5.0%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean94.19436159
Minimum33.3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:50.802459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum33.3
5-th percentile75
Q191.7
median100
Q3100
95-th percentile100
Maximum100
Range66.7
Interquartile range (IQR)8.3

Descriptive statistics

Standard deviation8.860533207
Coefficient of variation (CV)0.09406649248
Kurtosis5.736324255
Mean94.19436159
Median Absolute Deviation (MAD)0
Skewness-2.17625578
Sum461081.4
Variance78.50904871
MonotonicityNot monotonic
2023-07-21T13:17:50.899089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 2480
49.6%
95 73
 
1.5%
90 69
 
1.4%
91.7 66
 
1.3%
90.9 61
 
1.2%
92.9 54
 
1.1%
92.3 52
 
1.0%
83.3 52
 
1.0%
88.9 50
 
1.0%
95.2 50
 
1.0%
Other values (234) 1888
37.8%
(Missing) 105
 
2.1%
ValueCountFrequency (%)
33.3 1
< 0.1%
34.4 1
< 0.1%
36.8 1
< 0.1%
41.7 1
< 0.1%
42.9 1
< 0.1%
ValueCountFrequency (%)
100 2480
49.6%
98.7 1
 
< 0.1%
98.6 2
 
< 0.1%
98.5 2
 
< 0.1%
98.4 2
 
< 0.1%

percent_bc_gt_75
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct74
Distinct (%)1.5%
Missing124
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean42.12481952
Minimum0
Maximum100
Zeros1349
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:51.004563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median37.5
Q371.4
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)71.4

Descriptive statistics

Standard deviation36.09611308
Coefficient of variation (CV)0.8568846937
Kurtosis-1.246131468
Mean42.12481952
Median Absolute Deviation (MAD)37.5
Skewness0.3154959967
Sum205400.62
Variance1302.929379
MonotonicityNot monotonic
2023-07-21T13:17:51.105474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1349
27.0%
100 807
16.1%
50 531
 
10.6%
33.3 319
 
6.4%
66.7 297
 
5.9%
25 226
 
4.5%
75 176
 
3.5%
20 139
 
2.8%
40 124
 
2.5%
60 107
 
2.1%
Other values (64) 801
16.0%
(Missing) 124
 
2.5%
ValueCountFrequency (%)
0 1349
27.0%
0.5 1
 
< 0.1%
0.67 1
 
< 0.1%
0.75 1
 
< 0.1%
4.5 1
 
< 0.1%
ValueCountFrequency (%)
100 807
16.1%
90.9 3
 
0.1%
90 6
 
0.1%
88.9 3
 
0.1%
87.5 10
 
0.2%
Distinct5
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size78.1 KiB
0.0
4408 
1.0
555 
2.0
 
26
3.0
 
7
4.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14997
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4408
88.2%
1.0 555
 
11.1%
2.0 26
 
0.5%
3.0 7
 
0.1%
4.0 3
 
0.1%
(Missing) 1
 
< 0.1%

Length

2023-07-21T13:17:51.194693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:51.276489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4408
88.2%
1.0 555
 
11.1%
2.0 26
 
0.5%
3.0 7
 
0.1%
4.0 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 9407
62.7%
. 4999
33.3%
1 555
 
3.7%
2 26
 
0.2%
3 7
 
< 0.1%
4 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9998
66.7%
Other Punctuation 4999
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9407
94.1%
1 555
 
5.6%
2 26
 
0.3%
3 7
 
0.1%
4 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 4999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9407
62.7%
. 4999
33.3%
1 555
 
3.7%
2 26
 
0.2%
3 7
 
< 0.1%
4 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9407
62.7%
. 4999
33.3%
1 555
 
3.7%
2 26
 
0.2%
3 7
 
< 0.1%
4 3
 
< 0.1%

tax_liens
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0454
Minimum0
Maximum7
Zeros4861
Zeros (%)97.2%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:51.349736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3355016427
Coefficient of variation (CV)7.389904025
Kurtosis157.1691727
Mean0.0454
Median Absolute Deviation (MAD)0
Skewness11.1276604
Sum227
Variance0.1125613523
MonotonicityNot monotonic
2023-07-21T13:17:51.429700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4861
97.2%
1 94
 
1.9%
2 26
 
0.5%
3 7
 
0.1%
4 5
 
0.1%
6 3
 
0.1%
5 3
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 4861
97.2%
1 94
 
1.9%
2 26
 
0.5%
3 7
 
0.1%
4 5
 
0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 3
0.1%
5 3
0.1%
4 5
0.1%
3 7
0.1%

tot_hi_cred_lim
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4697
Distinct (%)96.0%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean177858.5986
Minimum1900
Maximum2193899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:51.527951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile18891
Q150350.5
median113374
Q3257039.5
95-th percentile517251.4
Maximum2193899
Range2191999
Interquartile range (IQR)206689

Descriptive statistics

Standard deviation176258.7749
Coefficient of variation (CV)0.9910050813
Kurtosis9.170905524
Mean177858.5986
Median Absolute Deviation (MAD)78204
Skewness2.184220621
Sum870617840
Variance3.106715574 × 1010
MonotonicityNot monotonic
2023-07-21T13:17:51.958169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34000 6
 
0.1%
21200 5
 
0.1%
29000 5
 
0.1%
13000 5
 
0.1%
24900 4
 
0.1%
19000 4
 
0.1%
14800 4
 
0.1%
64200 3
 
0.1%
36400 3
 
0.1%
21800 3
 
0.1%
Other values (4687) 4853
97.1%
(Missing) 105
 
2.1%
ValueCountFrequency (%)
1900 1
< 0.1%
2000 1
< 0.1%
2400 1
< 0.1%
2800 1
< 0.1%
3100 1
< 0.1%
ValueCountFrequency (%)
2193899 1
< 0.1%
1555818 1
< 0.1%
1513675 1
< 0.1%
1452426 1
< 0.1%
1443207 1
< 0.1%

total_bal_ex_mort
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4807
Distinct (%)97.6%
Missing75
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean50629.86294
Minimum0
Maximum503654
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:52.056145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6792.2
Q121087
median37348
Q362549
95-th percentile135631
Maximum503654
Range503654
Interquartile range (IQR)41462

Descriptive statistics

Standard deviation48457.26675
Coefficient of variation (CV)0.9570886415
Kurtosis14.21358119
Mean50629.86294
Median Absolute Deviation (MAD)19325
Skewness2.997002993
Sum249352075
Variance2348106700
MonotonicityNot monotonic
2023-07-21T13:17:52.156318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
0.1%
17784 3
 
0.1%
39150 2
 
< 0.1%
19974 2
 
< 0.1%
50467 2
 
< 0.1%
8185 2
 
< 0.1%
31468 2
 
< 0.1%
7744 2
 
< 0.1%
34109 2
 
< 0.1%
14338 2
 
< 0.1%
Other values (4797) 4902
98.0%
(Missing) 75
 
1.5%
ValueCountFrequency (%)
0 4
0.1%
34 1
 
< 0.1%
61 1
 
< 0.1%
81 1
 
< 0.1%
87 1
 
< 0.1%
ValueCountFrequency (%)
503654 1
< 0.1%
490077 1
< 0.1%
456634 1
< 0.1%
453441 1
< 0.1%
419773 1
< 0.1%

total_bc_limit
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct895
Distinct (%)18.2%
Missing75
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean23496.26964
Minimum0
Maximum262700
Zeros49
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:52.251431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2300
Q18500
median16500
Q331100
95-th percentile67700
Maximum262700
Range262700
Interquartile range (IQR)22600

Descriptive statistics

Standard deviation22931.61149
Coefficient of variation (CV)0.9759681788
Kurtosis11.35368752
Mean23496.26964
Median Absolute Deviation (MAD)9700
Skewness2.557697395
Sum115719128
Variance525858805.7
MonotonicityNot monotonic
2023-07-21T13:17:52.353617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49
 
1.0%
10000 40
 
0.8%
7500 37
 
0.7%
8000 36
 
0.7%
3000 34
 
0.7%
7000 32
 
0.6%
6000 32
 
0.6%
9000 31
 
0.6%
2000 31
 
0.6%
11500 31
 
0.6%
Other values (885) 4572
91.4%
(Missing) 75
 
1.5%
ValueCountFrequency (%)
0 49
1.0%
200 1
 
< 0.1%
300 4
 
0.1%
400 1
 
< 0.1%
500 12
 
0.2%
ValueCountFrequency (%)
262700 1
< 0.1%
233300 1
< 0.1%
226400 1
< 0.1%
198800 1
< 0.1%
193000 1
< 0.1%

total_il_high_credit_limit
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct3934
Distinct (%)80.4%
Missing105
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean43315.40429
Minimum0
Maximum402132
Zeros606
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-07-21T13:17:52.457852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114897
median32239
Q358184
95-th percentile125023.2
Maximum402132
Range402132
Interquartile range (IQR)43287

Descriptive statistics

Standard deviation44376.6619
Coefficient of variation (CV)1.024500697
Kurtosis10.56609259
Mean43315.40429
Median Absolute Deviation (MAD)20408
Skewness2.521000351
Sum212028904
Variance1969288121
MonotonicityNot monotonic
2023-07-21T13:17:52.560790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 606
 
12.1%
10000 33
 
0.7%
15000 26
 
0.5%
12000 23
 
0.5%
20000 14
 
0.3%
25000 13
 
0.3%
5000 12
 
0.2%
6000 11
 
0.2%
35000 10
 
0.2%
16000 10
 
0.2%
Other values (3924) 4137
82.7%
(Missing) 105
 
2.1%
ValueCountFrequency (%)
0 606
12.1%
500 1
 
< 0.1%
501 1
 
< 0.1%
598 1
 
< 0.1%
994 1
 
< 0.1%
ValueCountFrequency (%)
402132 1
< 0.1%
394018 1
< 0.1%
393842 1
< 0.1%
364540 1
< 0.1%
364219 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
False
4997 
True
 
3
ValueCountFrequency (%)
False 4997
99.9%
True 3
 
0.1%
2023-07-21T13:17:52.665965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Cash
4840 
DirectPay
 
160

Length

Max length9
Median length4
Mean length4.16
Min length4

Characters and Unicode

Total characters20800
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCash
2nd rowCash
3rd rowCash
4th rowCash
5th rowCash

Common Values

ValueCountFrequency (%)
Cash 4840
96.8%
DirectPay 160
 
3.2%

Length

2023-07-21T13:17:52.729516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T13:17:52.815769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
cash 4840
96.8%
directpay 160
 
3.2%

Most occurring characters

ValueCountFrequency (%)
a 5000
24.0%
C 4840
23.3%
s 4840
23.3%
h 4840
23.3%
D 160
 
0.8%
i 160
 
0.8%
r 160
 
0.8%
e 160
 
0.8%
c 160
 
0.8%
t 160
 
0.8%
Other values (2) 320
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15640
75.2%
Uppercase Letter 5160
 
24.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5000
32.0%
s 4840
30.9%
h 4840
30.9%
i 160
 
1.0%
r 160
 
1.0%
e 160
 
1.0%
c 160
 
1.0%
t 160
 
1.0%
y 160
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
C 4840
93.8%
D 160
 
3.1%
P 160
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 20800
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5000
24.0%
C 4840
23.3%
s 4840
23.3%
h 4840
23.3%
D 160
 
0.8%
i 160
 
0.8%
r 160
 
0.8%
e 160
 
0.8%
c 160
 
0.8%
t 160
 
0.8%
Other values (2) 320
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 5000
24.0%
C 4840
23.3%
s 4840
23.3%
h 4840
23.3%
D 160
 
0.8%
i 160
 
0.8%
r 160
 
0.8%
e 160
 
0.8%
c 160
 
0.8%
t 160
 
0.8%
Other values (2) 320
 
1.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
False
4905 
True
 
95
ValueCountFrequency (%)
False 4905
98.1%
True 95
 
1.9%
2023-07-21T13:17:52.889194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-07-21T13:17:53.087234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
loan_amntfunded_amntfunded_amnt_invint_rateinstallmentannual_incdtidelinq_2yrsfico_range_lowfico_range_highinq_last_6mthsmths_since_last_delinqmths_since_last_recordopen_accpub_recrevol_balrevol_utiltotal_accout_prncpout_prncp_invtotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_inttotal_rec_late_feerecoveriescollection_recovery_feelast_pymnt_amntlast_fico_range_highlast_fico_range_lowmths_since_last_major_derogtot_coll_amttot_cur_balopen_acc_6mopen_act_ilopen_il_12mopen_il_24mmths_since_rcnt_iltotal_bal_ilil_utilopen_rv_12mopen_rv_24mmax_bal_bcall_utiltotal_rev_hi_liminq_fitotal_cu_tlinq_last_12macc_open_past_24mthsavg_cur_balbc_open_to_buybc_utildelinq_amntmo_sin_old_il_acctmo_sin_old_rev_tl_opmo_sin_rcnt_rev_tl_opmo_sin_rcnt_tlmort_accmths_since_recent_bcmths_since_recent_bc_dlqmths_since_recent_inqmths_since_recent_revol_delinqnum_accts_ever_120_pdnum_actv_bc_tlnum_actv_rev_tlnum_bc_satsnum_bc_tlnum_il_tlnum_op_rev_tlnum_rev_acctsnum_rev_tl_bal_gt_0num_satsnum_tl_90g_dpd_24mnum_tl_op_past_12mpct_tl_nvr_dlqpercent_bc_gt_75tax_lienstot_hi_cred_limtotal_bal_ex_morttotal_bc_limittotal_il_high_credit_limittermgradesub_gradeemp_lengthhome_ownershipverification_statusloan_statuspymnt_planpurposeaddr_stateinitial_list_statuslast_pymnt_dnext_pymnt_dlast_credit_pull_dcollections_12_mths_ex_medapplication_typeacc_now_delinqchargeoff_within_12_mthsnum_tl_120dpd_2mnum_tl_30dpdpub_rec_bankruptcieshardship_flagdisbursement_methoddebt_settlement_flag
loan_amnt1.0001.0001.0000.0930.9650.4590.0470.0090.1390.139-0.032-0.023-0.0220.204-0.0790.4550.1230.2260.2230.2230.6500.6500.5290.6990.0340.0530.0570.4720.0850.0850.011-0.0800.334-0.0470.082-0.017-0.0010.0260.156-0.102-0.049-0.0450.3870.0110.417-0.0100.0920.019-0.0180.2820.2200.044-0.0230.1070.2070.0540.0430.2450.032-0.0570.007-0.031-0.0730.1910.1530.2220.2220.1100.1780.2020.1510.201-0.018-0.0360.0710.0220.0000.3620.3230.3990.1870.4370.0700.0680.0320.1250.1610.0540.0000.0980.0000.0910.0000.0240.0000.0000.1410.0000.0180.0000.0000.0350.0000.0910.044
funded_amnt1.0001.0001.0000.0930.9650.4590.0470.0090.1390.139-0.032-0.023-0.0220.204-0.0790.4550.1220.2260.2230.2230.6500.6500.5290.6990.0340.0540.0570.4720.0850.0850.011-0.0800.334-0.0470.082-0.017-0.0010.0260.156-0.102-0.049-0.0450.3870.0110.417-0.0100.0920.019-0.0180.2820.2200.044-0.0230.1070.2070.0540.0430.2450.032-0.0570.007-0.031-0.0730.1910.1530.2220.2220.1100.1780.2020.1510.201-0.018-0.0360.0710.0220.0000.3620.3230.3990.1870.4370.0690.0660.0320.1250.1610.0540.0000.0980.0000.0910.0000.0240.0000.0000.1410.0000.0180.0000.0000.0350.0000.0910.044
funded_amnt_inv1.0001.0001.0000.0930.9640.4590.0480.0080.1390.139-0.033-0.024-0.0180.204-0.0790.4560.1230.2260.2250.2250.6480.6490.5270.6980.0340.0530.0570.4710.0860.0860.011-0.0800.334-0.0470.082-0.017-0.0010.0260.157-0.102-0.049-0.0450.3880.0110.417-0.0100.0920.019-0.0180.2820.2210.044-0.0230.1070.2070.0530.0430.2450.032-0.0560.008-0.031-0.0740.1910.1530.2220.2220.1100.1780.2020.1510.201-0.018-0.0370.0720.0220.0000.3620.3230.3990.1870.4370.0690.0670.0320.1250.1610.0550.0000.0990.0000.0930.0000.0240.0000.0000.1410.0000.0160.0000.0000.0360.0000.0910.041
int_rate0.0930.0930.0931.0000.115-0.1330.1900.068-0.430-0.4300.171-0.0550.029-0.0170.048-0.0120.301-0.076-0.029-0.0290.0680.069-0.0800.4040.0770.1890.1830.035-0.412-0.412-0.0470.052-0.0880.1070.0690.1450.134-0.1490.0660.1720.1150.144-0.0770.341-0.2570.118-0.0050.1620.167-0.088-0.3860.3200.005-0.104-0.172-0.118-0.133-0.118-0.093-0.053-0.144-0.0470.0780.0340.108-0.069-0.1130.001-0.026-0.0820.103-0.0230.0580.173-0.0930.3010.017-0.1600.024-0.2860.0070.3710.7000.7340.0080.0470.1970.0990.0000.0610.0350.1440.0410.0000.0150.0070.0370.0150.0000.0150.0000.0260.0000.1750.063
installment0.9650.9650.9640.1151.0000.4400.0480.0200.0830.083-0.011-0.030-0.0340.195-0.0710.4350.1450.2070.1720.1720.6570.6570.5520.6670.0460.0580.0600.4950.0590.0590.019-0.0630.299-0.0310.081-0.0030.0110.0110.149-0.091-0.030-0.0220.3660.0400.3750.0020.0760.0410.0020.2480.1770.074-0.0240.0910.1770.0370.0270.2100.015-0.062-0.004-0.035-0.0610.1960.1620.2140.2110.0950.1740.1930.1590.192-0.016-0.0150.0500.0500.0090.3210.3030.3600.1720.3170.0790.0880.0320.0960.1580.0230.0000.0900.0000.0450.0000.0000.0000.0000.1020.0000.0000.0000.0000.0280.0000.0540.000
annual_inc0.4590.4590.459-0.1330.4401.000-0.2080.1010.0930.0930.038-0.072-0.1270.275-0.0520.3910.0690.3390.1000.1000.3000.3010.2760.2230.034-0.019-0.0120.2560.0730.073-0.003-0.0300.5150.0520.2480.1360.212-0.1490.349-0.103-0.017-0.0020.3370.0420.3960.1210.1280.1220.1180.4410.2220.0050.0220.2160.2480.031-0.0560.3450.007-0.047-0.064-0.0460.0520.1840.1370.2250.2540.2700.1660.2130.1360.2720.0220.083-0.070-0.0120.0360.5480.4910.3820.4070.0000.0000.0000.0000.0430.0640.0510.0000.0010.0000.0000.0600.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.000
dti0.0470.0470.0480.1900.048-0.2081.000-0.022-0.005-0.005-0.0240.0070.1190.311-0.0300.2640.1900.2330.0320.0320.0170.017-0.0240.1220.0120.0410.042-0.001-0.067-0.067-0.016-0.0510.1530.0150.4300.2030.278-0.2160.426-0.065-0.017-0.0100.1800.2180.1550.0910.1370.0580.1390.057-0.0560.1820.0250.0430.0700.004-0.047-0.005-0.0030.0150.0180.011-0.0770.1680.2510.1140.0910.2710.1860.1510.2490.307-0.0140.1000.0970.171-0.0340.1530.4530.0860.4670.0250.0000.0240.0270.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.2780.0000.0000.0000.0000.0000.0000.0000.000
delinq_2yrs0.0090.0090.0080.0680.0200.101-0.0221.000-0.226-0.2260.024-0.821-0.0550.063-0.052-0.0260.0220.125-0.035-0.0350.0370.0370.0260.0630.0700.0250.0280.002-0.138-0.138-0.5530.0360.0860.0080.074-0.004-0.0190.0230.063-0.024-0.025-0.052-0.0550.035-0.0570.0100.0210.059-0.0570.069-0.0710.0250.1100.1000.1320.0300.0180.1030.043-0.650-0.042-0.7100.209-0.0250.026-0.0360.0400.0780.0210.0960.0240.0580.518-0.042-0.4890.0120.0140.0870.042-0.0850.0650.0120.0000.0000.0060.0000.0050.0350.0000.0000.0800.0000.0000.0000.0000.0000.0000.0380.1580.0000.0460.0000.0000.0000.000
fico_range_low0.1390.1390.139-0.4300.0830.093-0.005-0.2261.0001.000-0.1110.1100.2770.054-0.2240.030-0.4190.0450.1280.1280.0300.0290.069-0.102-0.048-0.120-0.1180.0530.4400.4400.065-0.2500.145-0.0710.011-0.0160.001-0.0040.029-0.128-0.135-0.1620.088-0.3930.377-0.0480.041-0.145-0.1140.1360.501-0.441-0.0190.0280.1410.1130.0750.1030.0730.0680.0610.073-0.293-0.089-0.1660.0770.0490.0240.0330.024-0.1600.066-0.150-0.0990.370-0.402-0.0870.2400.0730.3830.0900.0540.1940.1750.0180.0670.1370.0600.0000.0450.0300.1090.0490.0000.0610.0610.1170.0110.0270.0000.0000.0940.0000.0060.000
fico_range_high0.1390.1390.139-0.4300.0830.093-0.005-0.2261.0001.000-0.1110.1100.2770.054-0.2240.030-0.4190.0450.1280.1280.0300.0290.069-0.102-0.048-0.120-0.1180.0530.4400.4400.065-0.2500.145-0.0710.011-0.0160.001-0.0040.029-0.128-0.135-0.1620.088-0.3930.377-0.0480.041-0.145-0.1140.1360.501-0.441-0.0190.0280.1410.1130.0750.1030.0730.0680.0610.073-0.293-0.089-0.1660.0770.0490.0240.0330.024-0.1600.066-0.150-0.0990.370-0.402-0.0870.2400.0730.3830.0900.0520.1940.1750.0130.0650.1360.0590.0000.0470.0280.1090.0460.0000.0590.0610.1170.0110.0270.0000.0000.0940.0000.0000.004
inq_last_6mths-0.032-0.032-0.0330.171-0.0110.038-0.0240.024-0.111-0.1111.0000.024-0.0650.1330.101-0.069-0.0980.153-0.088-0.0880.0070.007-0.0080.0210.0200.0760.0760.039-0.115-0.115-0.0060.0310.0340.4180.0530.1490.127-0.1420.0650.1020.2950.272-0.099-0.039-0.0070.1860.0390.5260.279-0.0170.020-0.085-0.0300.004-0.027-0.309-0.3390.025-0.2190.068-0.6870.0530.0580.0330.0780.0780.1120.0920.1290.1390.0670.1350.0310.330-0.032-0.0750.0170.0320.018-0.0370.0310.0190.0830.0960.0000.0050.0470.0930.0000.0260.0280.0830.1200.4970.0910.0070.0260.0000.0000.0000.0000.0870.0000.0150.000
mths_since_last_delinq-0.023-0.023-0.024-0.055-0.030-0.0720.007-0.8210.1100.1100.0241.000-0.100-0.0580.104-0.039-0.027-0.0640.0510.051-0.061-0.061-0.050-0.078-0.058-0.008-0.012-0.0220.1160.1160.7140.065-0.0900.037-0.0480.0360.055-0.042-0.0380.0430.0580.1050.005-0.026-0.0200.022-0.0010.0070.137-0.0740.020-0.031-0.128-0.036-0.092-0.065-0.061-0.068-0.0720.7640.0140.8480.0890.027-0.0110.030-0.002-0.026-0.021-0.060-0.018-0.052-0.3560.1050.194-0.0130.018-0.100-0.044-0.000-0.0430.0000.0000.0420.0260.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0430.1960.0470.0230.1560.1060.0000.0000.038
mths_since_last_record-0.022-0.022-0.0180.029-0.034-0.1270.119-0.0550.2770.277-0.065-0.1001.0000.035-0.3600.050-0.002-0.1610.0760.076-0.150-0.148-0.145-0.112-0.080-0.037-0.048-0.0010.0780.0780.008-0.0270.056-0.0840.0260.026-0.0300.0180.0140.003-0.095-0.1870.054-0.0060.052-0.126-0.005-0.104-0.0690.0390.0060.024NaN-0.149-0.1740.0210.004-0.0900.014-0.1610.033-0.128-0.173-0.012-0.006-0.005-0.188-0.0540.024-0.148-0.0010.046-0.039-0.0060.1960.015-0.3460.051-0.0090.021-0.0060.0000.0000.0000.0600.0000.0000.0450.1190.0000.0000.0840.0920.1460.0540.0540.0001.0000.0001.0001.0000.2520.1190.0950.000
open_acc0.2040.2040.204-0.0170.1950.2750.3110.0630.0540.0540.133-0.0580.0351.000-0.0300.391-0.1300.7170.0070.0070.1590.1590.1360.145-0.0120.0240.0320.1240.0070.007-0.022-0.0050.3580.2770.4570.1720.247-0.1630.3730.0500.3320.4150.194-0.0400.5410.1330.1500.1950.4690.0170.334-0.1170.0100.1350.184-0.276-0.2520.183-0.264-0.015-0.089-0.042-0.0030.5430.6680.6480.5580.3880.8490.6860.6680.9990.0090.3540.040-0.078-0.0190.4100.4710.4200.3880.0810.0110.0110.0000.0850.0050.0000.0000.0110.0000.0290.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.000
pub_rec-0.079-0.079-0.0790.048-0.071-0.052-0.030-0.052-0.224-0.2240.1010.104-0.360-0.0301.000-0.168-0.0900.015-0.056-0.056-0.031-0.030-0.030-0.0230.0320.0100.009-0.003-0.089-0.0890.1140.039-0.1040.085-0.0140.0580.057-0.052-0.0160.0160.0980.120-0.111-0.034-0.1390.0810.0030.1250.116-0.095-0.073-0.040-0.0240.0530.036-0.094-0.083-0.011-0.0410.103-0.0620.0890.001-0.066-0.015-0.0580.013-0.0100.0070.048-0.020-0.038-0.0360.1010.019-0.0390.418-0.106-0.106-0.164-0.0380.0000.0260.0320.0080.0000.0310.0090.0000.0000.0610.0000.0000.0250.0000.0000.0000.0000.0070.0000.0000.6270.0000.0000.000
revol_bal0.4550.4550.456-0.0120.4350.3910.264-0.0260.0300.030-0.069-0.0390.0500.391-0.1681.0000.4530.3210.0770.0770.3420.3420.2960.334-0.0170.0010.0020.2030.0750.0750.013-0.1660.402-0.0790.062-0.073-0.0450.0820.099-0.175-0.0090.0120.7840.1950.729-0.0850.080-0.072-0.0250.2850.2010.3450.0180.1630.3280.0350.0800.268-0.003-0.0200.080-0.057-0.1470.4920.5030.4250.3830.0750.4040.3730.5040.393-0.062-0.0530.1460.311-0.0140.4220.4700.6480.1380.0670.0000.0000.0250.0910.0460.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.000
revol_util0.1230.1220.1230.3010.1450.0690.1900.022-0.419-0.419-0.098-0.027-0.002-0.130-0.0900.4531.000-0.088-0.048-0.0480.1670.1670.1150.2700.0410.0630.0570.034-0.211-0.211-0.031-0.0920.114-0.2190.065-0.081-0.0610.0880.032-0.072-0.206-0.2260.3860.689-0.183-0.102-0.026-0.129-0.2390.179-0.6100.8640.0060.0670.0300.2070.2070.0410.188-0.0260.114-0.0130.0060.1330.133-0.115-0.0980.013-0.200-0.1530.135-0.1330.002-0.223-0.0500.748-0.0030.0100.178-0.1430.0370.0720.1280.1110.0120.0220.1060.0350.0000.0640.0570.0820.0370.0410.0280.0250.0320.0360.0000.0000.0000.0380.0000.0340.027
total_acc0.2260.2260.226-0.0760.2070.3390.2330.1250.0450.0450.153-0.064-0.1610.7170.0150.321-0.0881.000-0.060-0.0600.2150.2150.2010.142-0.0020.0290.0350.1820.0310.031-0.068-0.0120.4430.2360.4170.2200.318-0.1930.4180.0530.1920.2480.177-0.0010.4260.1570.2790.2150.3980.2140.236-0.0860.0090.3530.359-0.176-0.2100.416-0.1510.005-0.113-0.0120.1190.2860.3960.3930.6250.6620.5670.7660.3950.7150.0660.299-0.080-0.051-0.0180.4790.4850.3060.4210.0990.0260.0390.0510.1520.0260.0390.0000.0180.0320.0000.0000.0490.0000.0000.0000.0000.0310.0000.0000.0210.0000.0000.000
out_prncp0.2230.2230.225-0.0290.1720.1000.032-0.0350.1280.128-0.0880.0510.0760.007-0.0560.077-0.048-0.0601.0001.000-0.338-0.337-0.3940.055-0.054-0.257-0.252-0.3000.1560.1560.010-0.0140.038-0.0910.024-0.064-0.0530.0470.020-0.095-0.068-0.0830.170-0.0500.1340.0050.030-0.068-0.0620.0310.143-0.086-0.0190.0230.0060.0730.066-0.0300.039-0.0080.0340.019-0.0310.0330.0010.043-0.071-0.015-0.001-0.073-0.0000.003-0.006-0.0640.037-0.100-0.0320.0700.0520.1440.0430.2950.0280.0280.0100.0710.0700.3230.0610.0210.0080.2420.2100.0000.1190.0000.2570.0000.0000.0000.0000.0000.0610.2410.082
out_prncp_inv0.2230.2230.225-0.0290.1720.1000.032-0.0350.1280.128-0.0880.0510.0760.007-0.0560.077-0.048-0.0601.0001.000-0.338-0.337-0.3940.055-0.054-0.257-0.252-0.3000.1560.1560.010-0.0140.038-0.0910.024-0.064-0.0530.0470.020-0.095-0.068-0.0830.170-0.0500.1340.0050.030-0.068-0.0620.0310.143-0.086-0.0190.0230.0060.0730.066-0.0300.039-0.0080.0340.019-0.0310.0330.0010.043-0.071-0.015-0.001-0.073-0.0000.003-0.006-0.0640.037-0.100-0.0320.0700.0520.1440.0430.2950.0280.0280.0100.0710.0700.3230.0610.0210.0080.2420.2100.0000.1190.0000.2570.0000.0000.0000.0000.0000.0610.2410.082
total_pymnt0.6500.6500.6480.0680.6570.3000.0170.0370.0300.0300.007-0.061-0.1500.159-0.0310.3420.1670.215-0.338-0.3381.0001.0000.9620.7300.041-0.060-0.0550.5940.1100.110-0.016-0.0550.239-0.0240.049-0.0060.0170.0190.115-0.038-0.0120.0050.2470.0580.246-0.0300.0300.036-0.0190.2040.0700.121-0.0130.0810.1870.0120.0250.2240.015-0.0420.004-0.049-0.0390.1520.1440.1500.2190.0810.1460.2090.1410.158-0.005-0.0290.0190.1090.0380.2390.2240.2290.1140.2120.0810.1100.0370.0930.1660.1760.0000.0540.0170.1090.1050.0000.0890.0080.0730.0000.0100.0000.0000.0270.0000.1740.033
total_pymnt_inv0.6500.6500.6490.0690.6570.3010.0170.0370.0290.0290.007-0.061-0.1480.159-0.0300.3420.1670.215-0.337-0.3371.0001.0000.9620.7310.041-0.060-0.0550.5950.1100.110-0.016-0.0550.239-0.0240.049-0.0060.0170.0190.115-0.038-0.0120.0050.2480.0580.246-0.0300.0300.036-0.0190.2040.0700.121-0.0130.0810.1870.0120.0250.2240.015-0.0420.004-0.049-0.0390.1520.1440.1500.2190.0810.1460.2090.1410.158-0.005-0.0290.0190.1090.0380.2390.2240.2290.1140.2120.0810.1100.0370.0940.1660.1750.0000.0540.0180.1080.1050.0000.0890.0080.0730.0000.0090.0000.0000.0270.0000.1740.033
total_rec_prncp0.5290.5290.527-0.0800.5520.276-0.0240.0260.0690.069-0.008-0.050-0.1450.136-0.0300.2960.1150.201-0.394-0.3940.9620.9621.0000.5690.004-0.198-0.1940.6210.2030.2030.004-0.0520.217-0.0310.023-0.021-0.0010.0370.082-0.056-0.025-0.0100.2170.0050.235-0.0410.0190.014-0.0400.1880.0960.078-0.0100.0770.1860.0210.0370.2160.025-0.0330.020-0.035-0.0410.1240.1070.1350.2120.0680.1290.1990.1050.135-0.018-0.0460.0250.0700.0370.2220.1860.2260.0910.1180.0490.0510.0240.0940.1430.2250.0000.0480.0080.1340.1340.0000.1110.0080.0880.0000.0320.0000.0000.0330.0000.1680.048
total_rec_int0.6990.6990.6980.4040.6670.2230.1220.063-0.102-0.1020.021-0.078-0.1120.145-0.0230.3340.2700.1420.0550.0550.7300.7310.5691.0000.0940.0740.0740.262-0.103-0.103-0.065-0.0320.186-0.0290.076-0.0030.0100.0100.123-0.0060.0120.0270.2510.1740.155-0.0040.0110.052-0.0040.149-0.0530.222-0.0110.0560.1100.0000.0130.1420.003-0.059-0.011-0.070-0.0180.1750.1930.1190.1290.0630.1220.1340.1890.1420.030-0.008-0.0070.1970.0320.1670.2230.1320.1150.5030.2160.2350.0330.0580.1700.1050.0000.0000.0000.0590.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0840.052
total_rec_late_fee0.0340.0340.0340.0770.0460.0340.0120.070-0.048-0.0480.020-0.058-0.080-0.0120.032-0.0170.041-0.002-0.054-0.0540.0410.0410.0040.0941.0000.1860.185-0.075-0.206-0.206-0.0720.0240.0210.0090.0250.0360.016-0.0280.0580.024-0.022-0.021-0.0020.068-0.0420.0280.0030.0280.0080.023-0.0530.032-0.0120.0110.005-0.012-0.018-0.018-0.031-0.099-0.014-0.0710.039-0.0090.001-0.027-0.0200.021-0.027-0.0210.002-0.0080.0510.018-0.0600.0270.0700.0140.041-0.0570.0400.0430.0610.0920.0000.0000.0250.1320.0510.0000.0000.0240.0850.0000.0000.0000.0480.0000.0000.0000.0000.0000.0510.0000.124
recoveries0.0530.0540.0530.1890.058-0.0190.0410.025-0.120-0.1200.076-0.008-0.0370.0240.0100.0010.0630.029-0.257-0.257-0.060-0.060-0.1980.0740.1861.0000.984-0.149-0.430-0.430-0.0180.014-0.0110.0700.0150.0590.046-0.0560.0320.0690.0500.054-0.0370.085-0.0560.0260.0080.0850.092-0.019-0.0850.068-0.0170.001-0.023-0.060-0.081-0.003-0.0660.009-0.049-0.0100.0500.0230.043-0.0020.0210.0300.0190.0240.0420.0280.0260.088-0.0380.076-0.002-0.0310.014-0.0700.0050.1550.0960.1130.0000.0000.0600.2370.0000.0000.0000.0080.1121.0000.0990.0360.0000.0000.0000.0470.0000.0000.0000.0000.428
collection_recovery_fee0.0570.0570.0570.1830.060-0.0120.0420.028-0.118-0.1180.076-0.012-0.0480.0320.0090.0020.0570.035-0.252-0.252-0.055-0.055-0.1940.0740.1850.9841.000-0.144-0.424-0.424-0.0170.016-0.0080.0700.0150.0590.046-0.0560.0320.0690.0500.054-0.0370.085-0.0540.0260.0080.0850.094-0.018-0.0820.064-0.0170.005-0.019-0.064-0.082-0.001-0.0690.009-0.052-0.0070.0550.0250.046-0.0000.0220.0330.0230.0270.0450.0330.0280.090-0.0420.069-0.001-0.0270.017-0.0700.0100.1490.0920.1100.0000.0000.0600.2280.0000.0000.0000.0070.1071.0000.0890.0460.0000.0000.0000.0490.0000.0000.0000.0000.434
last_pymnt_amnt0.4720.4720.4710.0350.4950.256-0.0010.0020.0530.0530.039-0.022-0.0010.124-0.0030.2030.0340.182-0.300-0.3000.5940.5950.6210.262-0.075-0.149-0.1441.0000.1780.1780.010-0.0550.2110.0160.0660.0480.060-0.0360.134-0.040-0.0150.0100.2100.0060.2040.0290.0830.0790.0590.1830.1080.008-0.0070.0350.101-0.003-0.0130.187-0.012-0.038-0.039-0.017-0.0190.0750.0580.1140.1600.1030.1050.1550.0540.122-0.0260.0290.0380.0110.0050.2160.1870.1900.1180.1970.0580.0660.0000.0630.0690.2420.0000.0100.0000.0780.2000.0000.1880.0000.0680.0190.0350.0000.0000.0000.0000.0740.062
last_fico_range_high0.0850.0850.086-0.4120.0590.073-0.067-0.1380.4400.440-0.1150.1160.0780.007-0.0890.075-0.2110.0310.1560.1560.1100.1100.203-0.103-0.206-0.430-0.4240.1781.0001.0000.089-0.1110.090-0.114-0.057-0.090-0.0890.091-0.042-0.113-0.120-0.1360.123-0.2700.265-0.0990.026-0.160-0.1500.0980.322-0.229-0.0310.0480.1630.0960.1080.1170.0910.0740.0880.080-0.129-0.043-0.1040.0540.057-0.0270.0110.036-0.1040.006-0.084-0.1430.186-0.219-0.0320.1500.0080.2860.0020.0660.1780.1580.0220.0650.1070.3040.0460.0440.0050.1050.1200.0000.1320.0000.0510.0450.0400.0130.0390.0580.0460.0750.214
last_fico_range_low0.0850.0850.086-0.4120.0590.073-0.067-0.1380.4400.440-0.1150.1160.0780.007-0.0890.075-0.2110.0310.1560.1560.1100.1100.203-0.103-0.206-0.430-0.4240.1781.0001.0000.089-0.1110.090-0.114-0.057-0.090-0.0890.091-0.042-0.113-0.120-0.1360.123-0.2700.265-0.0990.026-0.160-0.1500.0980.322-0.229-0.0310.0480.1630.0960.1080.1170.0910.0740.0880.080-0.129-0.043-0.1040.0540.057-0.0270.0110.036-0.1040.006-0.084-0.1430.186-0.219-0.0320.1500.0080.2860.0020.0730.1790.1980.0260.0650.0940.3220.0000.0440.0270.0920.1530.0000.1580.0080.0550.0370.0060.0000.0330.0540.0000.0680.208
mths_since_last_major_derog0.0110.0110.011-0.0470.019-0.003-0.016-0.5530.0650.065-0.0060.7140.008-0.0220.1140.013-0.031-0.0680.0100.010-0.016-0.0160.004-0.065-0.072-0.018-0.0170.0100.0890.0891.0000.064-0.0880.002-0.087-0.0140.055-0.021-0.0620.0000.0500.1330.033-0.0660.0450.044-0.0190.0260.142-0.0940.033-0.011-0.084-0.024-0.001-0.046-0.054-0.068-0.0380.5640.0060.558-0.0330.0720.0170.0930.044-0.0890.047-0.0000.025-0.019-0.6910.0690.1340.0260.016-0.080-0.0470.068-0.0510.0580.0000.0000.0230.0760.0000.0000.0000.0000.0000.0000.0001.0000.0000.1490.0940.1990.2070.1191.0000.0830.0000.0000.030
tot_coll_amt-0.080-0.080-0.0800.052-0.063-0.030-0.0510.036-0.250-0.2500.0310.065-0.027-0.0050.039-0.166-0.092-0.012-0.014-0.014-0.055-0.055-0.052-0.0320.0240.0140.016-0.055-0.111-0.1110.0641.000-0.0420.0400.010-0.017-0.0040.021-0.0070.0080.0510.072-0.167-0.035-0.1400.041-0.0160.0530.066-0.045-0.043-0.0840.0060.016-0.030-0.049-0.022-0.040-0.0370.062-0.0240.0720.119-0.046-0.016-0.048-0.0530.017-0.002-0.020-0.018-0.0040.0120.050-0.148-0.0810.037-0.044-0.060-0.1480.0040.0010.0000.0000.0000.0120.0000.0000.2850.0000.0000.0000.0001.0000.0000.0570.0000.0000.0000.0000.0000.0000.2850.0000.000
tot_cur_bal0.3340.3340.334-0.0880.2990.5150.1530.0860.1450.1450.034-0.0900.0560.358-0.1040.4020.1140.4430.0380.0380.2390.2390.2170.1860.021-0.011-0.0080.2110.0900.090-0.088-0.0421.0000.1000.3590.1980.267-0.1990.4990.102-0.014-0.0240.3160.1940.3690.1690.2120.1870.1710.9270.1460.0590.0190.2250.2370.028-0.0900.6560.026-0.040-0.053-0.032-0.0000.0990.1190.1320.1910.3770.1430.2120.1200.3580.0340.142-0.0290.053-0.0400.9710.6180.2990.4970.1170.0250.0000.0590.2800.0570.0330.0000.0580.0230.0510.0001.0000.0000.0000.0770.0000.0000.0000.0000.0160.0000.0000.000
open_acc_6m-0.047-0.047-0.0470.107-0.0310.0520.0150.008-0.071-0.0710.4180.037-0.0840.2770.085-0.079-0.2190.236-0.091-0.091-0.024-0.024-0.031-0.0290.0090.0700.0700.016-0.114-0.1140.0020.0400.1001.0000.1260.3470.258-0.3830.1570.2270.5920.472-0.129-0.0680.0660.1650.0770.3540.546-0.0020.121-0.202-0.0300.014-0.024-0.671-0.8480.071-0.4900.073-0.3200.0590.0800.0780.1530.1620.1540.1490.2520.2160.1460.2760.0190.714-0.049-0.1650.0260.1040.0870.0140.1040.0000.0620.0810.0160.0000.0530.0370.0000.0000.0000.0370.1891.0000.0650.0000.0000.0000.0000.0000.0000.0670.0000.0000.081
open_act_il0.0820.0820.0820.0690.0810.2480.4300.0740.0110.0110.053-0.0480.0260.457-0.0140.0620.0650.4170.0240.0240.0490.0490.0230.0760.0250.0150.0150.066-0.057-0.057-0.0870.0100.3590.1261.0000.4020.546-0.4270.7680.203-0.010-0.0070.0460.3070.0330.2120.2060.1660.2630.204-0.0260.0550.0320.185-0.0200.016-0.1350.0600.001-0.023-0.030-0.0150.0580.0380.0560.0400.0400.6780.0530.0580.0560.4580.0370.200-0.0580.071-0.0200.3350.6470.0180.7880.0480.0000.0000.0380.0680.0060.0000.0000.0260.0000.0000.0001.0000.0000.0000.0000.0000.0460.0000.0000.0000.0000.0430.000
open_il_12m-0.017-0.017-0.0170.145-0.0030.1360.203-0.004-0.016-0.0160.1490.0360.0260.1720.058-0.073-0.0810.220-0.064-0.064-0.006-0.006-0.021-0.0030.0360.0590.0590.048-0.090-0.090-0.014-0.0170.1980.3470.4021.0000.697-0.8580.4310.4080.0520.063-0.0600.201-0.0190.3120.1650.3760.4040.1410.030-0.082-0.0020.028-0.032-0.034-0.3660.073-0.0450.094-0.1650.0770.065-0.039-0.0330.0130.0210.3640.0110.024-0.0330.1720.0120.540-0.011-0.080-0.0110.1680.328-0.0270.3530.0000.0840.1210.0620.0000.0430.0270.0000.0000.0970.0350.2111.0000.0900.0000.0000.0000.0630.0000.0000.0000.0000.0000.000
open_il_24m-0.001-0.001-0.0010.1340.0110.2120.278-0.0190.0010.0010.1270.055-0.0300.2470.057-0.045-0.0610.318-0.053-0.0530.0170.017-0.0010.0100.0160.0460.0460.060-0.089-0.0890.055-0.0040.2670.2580.5460.6971.000-0.7720.5610.3430.0460.076-0.0580.2220.0020.4050.2540.3160.5510.1910.027-0.067-0.0210.046-0.009-0.028-0.2710.107-0.0460.142-0.1160.0860.043-0.025-0.0060.0290.0360.5190.0460.046-0.0060.247-0.0350.403-0.001-0.059-0.0170.2400.434-0.0210.5000.0150.0790.1140.0310.0290.0520.0340.0000.0440.0430.0000.1991.0000.1110.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
mths_since_rcnt_il0.0260.0260.026-0.1490.011-0.149-0.2160.023-0.004-0.004-0.142-0.0420.018-0.163-0.0520.0820.088-0.1930.0470.0470.0190.0190.0370.010-0.028-0.056-0.056-0.0360.0910.091-0.0210.021-0.199-0.383-0.427-0.858-0.7721.000-0.481-0.449-0.046-0.0660.066-0.2040.020-0.325-0.150-0.329-0.418-0.143-0.0340.0870.006-0.0050.0550.0300.418-0.0510.053-0.1060.179-0.090-0.0340.0440.040-0.012-0.001-0.367-0.0040.0020.045-0.1630.007-0.459-0.0100.0780.009-0.168-0.3480.030-0.3920.0000.0000.0000.0250.0270.0190.0090.0000.0000.0270.0000.0721.0000.0760.0000.0000.0130.0000.0000.0410.0000.0000.0480.000
total_bal_il0.1560.1560.1570.0660.1490.3490.4260.0630.0290.0290.065-0.0380.0140.373-0.0160.0990.0320.4180.0200.0200.1150.1150.0820.1230.0580.0320.0320.134-0.042-0.042-0.062-0.0070.4990.1570.7680.4310.561-0.4811.0000.3950.0040.0240.0860.3380.0940.2590.2210.2400.2970.3910.0350.0100.0200.2150.0170.010-0.1630.123-0.0110.024-0.0600.0270.0630.0380.0500.0640.0740.6330.0630.0800.0480.3730.0510.233-0.0410.015-0.0240.4560.8720.0720.9540.0880.0150.0000.0280.1020.0400.0000.0000.0170.0420.0150.0001.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0760.000
il_util-0.102-0.102-0.1020.172-0.091-0.103-0.065-0.024-0.128-0.1280.1020.0430.0030.0500.016-0.175-0.0720.053-0.095-0.095-0.038-0.038-0.056-0.0060.0240.0690.069-0.040-0.113-0.1130.0000.0080.1020.2270.2030.4080.343-0.4490.3951.0000.0870.104-0.1490.537-0.1580.1650.0210.2100.2500.081-0.048-0.066-0.029-0.030-0.145-0.068-0.232-0.042-0.0660.080-0.0940.0640.071-0.080-0.071-0.066-0.0770.181-0.061-0.077-0.0750.0510.0130.284-0.020-0.075-0.025-0.0030.233-0.1510.1300.0000.0560.0480.0260.0400.0990.0370.0000.0000.0000.0490.0001.0000.0000.0410.0000.0000.0080.0000.0000.0000.0000.0820.000
open_rv_12m-0.049-0.049-0.0490.115-0.030-0.017-0.017-0.025-0.135-0.1350.2950.058-0.0950.3320.098-0.009-0.2060.192-0.068-0.068-0.012-0.012-0.0250.012-0.0220.0500.050-0.015-0.120-0.1200.0500.051-0.0140.592-0.0100.0520.046-0.0460.0040.0871.0000.758-0.091-0.1450.1280.0980.0210.3330.616-0.1450.146-0.173-0.009-0.049-0.054-0.830-0.588-0.008-0.6830.085-0.2030.0660.0710.2260.3190.3010.245-0.0130.4100.3100.3140.331-0.0100.804-0.028-0.1450.0450.007-0.0210.072-0.0270.0000.0400.0670.0000.0230.0470.0000.0000.0280.0000.0060.0491.0000.0000.0000.0390.0000.0260.0000.0000.0840.0000.0000.000
open_rv_24m-0.045-0.045-0.0450.144-0.022-0.002-0.010-0.052-0.162-0.1620.2720.105-0.1870.4150.1200.012-0.2260.248-0.083-0.0830.0050.005-0.0100.027-0.0210.0540.0540.010-0.136-0.1360.1330.072-0.0240.472-0.0070.0630.076-0.0660.0240.1040.7581.000-0.106-0.1550.1570.1410.0310.3130.813-0.1900.164-0.183-0.032-0.047-0.090-0.687-0.475-0.026-0.6310.188-0.1620.1150.0810.2980.4120.3690.314-0.0100.5160.3980.4070.414-0.0330.632-0.017-0.1450.045-0.0010.0010.085-0.0130.0000.0590.0660.0000.0000.0630.0200.0000.0000.0000.0340.0451.0000.0000.0370.0460.0000.0000.0000.0000.0980.0000.0000.000
max_bal_bc0.3870.3870.388-0.0770.3660.3370.180-0.0550.0880.088-0.0990.0050.0540.194-0.1110.7840.3860.1770.1700.1700.2470.2480.2170.251-0.002-0.037-0.0370.2100.1230.1230.033-0.1670.316-0.1290.046-0.060-0.0580.0660.086-0.149-0.091-0.1061.0000.1500.565-0.106-0.020-0.112-0.1050.2720.2130.384-0.0130.1390.2630.1110.1260.2050.075-0.0230.104-0.036-0.1460.3110.2080.2910.2770.0450.1760.1940.2060.195-0.082-0.1050.1300.306-0.0260.3370.3750.6530.1380.0710.0000.0000.0420.0640.0360.0000.0000.0210.0310.0270.1411.0000.2580.0000.0000.0000.0000.0000.0000.0000.0000.0890.000
all_util0.0110.0110.0110.3410.0400.0420.2180.035-0.393-0.393-0.039-0.026-0.006-0.040-0.0340.1950.689-0.001-0.050-0.0500.0580.0580.0050.1740.0680.0850.0850.006-0.270-0.270-0.066-0.0350.194-0.0680.3070.2010.222-0.2040.3380.537-0.145-0.1550.1501.000-0.2920.0640.0290.035-0.0190.230-0.5300.6080.0180.057-0.0620.1520.0580.0190.1450.0040.0360.0180.0840.0080.017-0.171-0.1720.214-0.221-0.1960.023-0.0410.029-0.019-0.0900.514-0.0080.0450.372-0.2750.2380.0370.1390.1230.0060.0230.1310.0560.0000.0370.0110.0440.0391.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
total_rev_hi_lim0.4170.4170.417-0.2570.3750.3960.155-0.0570.3770.377-0.007-0.0200.0520.541-0.1390.729-0.1830.4260.1340.1340.2460.2460.2350.155-0.042-0.056-0.0540.2040.2650.2650.045-0.1400.3690.0660.033-0.0190.0020.0200.094-0.1580.1280.1570.565-0.2921.000-0.0330.109-0.0030.1330.1920.689-0.2320.0150.1290.351-0.089-0.0470.278-0.124-0.0080.015-0.054-0.1930.4390.4360.5790.5120.0750.5950.5270.4360.542-0.0790.0920.223-0.174-0.0200.4860.3850.8810.1390.0570.0930.0680.0280.1000.0220.0460.0000.0000.0000.0650.0001.0000.0780.0000.0740.0000.0000.0000.0000.0520.0000.0360.000
inq_fi-0.010-0.010-0.0100.1180.0020.1210.0910.010-0.048-0.0480.1860.022-0.1260.1330.081-0.085-0.1020.1570.0050.005-0.030-0.030-0.041-0.0040.0280.0260.0260.029-0.099-0.0990.0440.0410.1690.1650.2120.3120.405-0.3250.2590.1650.0980.141-0.1060.064-0.0331.0000.1010.4850.3280.1300.034-0.109-0.0180.006-0.036-0.072-0.1660.102-0.0860.120-0.2070.0620.0750.0040.0080.0380.0460.2160.0620.0440.0150.135-0.0220.260-0.042-0.1040.0350.1530.176-0.0420.2280.0340.0700.0890.0000.0410.0240.0560.0000.0680.0000.0000.1051.0000.0450.0170.0180.0000.0000.0000.0000.1930.0000.0080.000
total_cu_tl0.0920.0920.092-0.0050.0760.1280.1370.0210.0410.0410.039-0.001-0.0050.1500.0030.080-0.0260.2790.0300.0300.0300.0300.0190.0110.0030.0080.0080.0830.0260.026-0.019-0.0160.2120.0770.2060.1650.254-0.1500.2210.0210.0210.031-0.0200.0290.1090.1011.0000.1090.1760.1760.023-0.047-0.0200.1160.090-0.018-0.0700.196-0.0070.029-0.054-0.0160.004-0.0920.012-0.0460.0420.3230.0760.1430.0180.150-0.0010.120-0.012-0.027-0.0390.2150.209-0.0060.2250.0630.0000.0000.0000.0670.0300.0000.0000.0300.1130.0350.0791.0000.0560.0000.0680.0000.0880.0000.0000.0000.0000.0000.000
inq_last_12m0.0190.0190.0190.1620.0410.1220.0580.059-0.145-0.1450.5260.007-0.1040.1950.125-0.072-0.1290.215-0.068-0.0680.0360.0360.0140.0520.0280.0850.0850.079-0.160-0.1600.0260.0530.1870.3540.1660.3760.316-0.3290.2400.2100.3330.313-0.1120.035-0.0030.4850.1091.0000.4390.1230.037-0.119-0.0200.011-0.046-0.268-0.3550.130-0.2240.074-0.6620.0230.1060.0320.0840.0950.1070.1930.1470.1410.0800.1940.0360.511-0.077-0.1150.0180.1720.171-0.0400.1920.0260.0770.1150.0000.0900.0610.0550.0000.0090.1030.0390.0781.0000.0000.0000.0000.0000.0000.0000.0000.1330.0000.0000.000
acc_open_past_24mths-0.018-0.018-0.0180.1670.0020.1180.139-0.057-0.114-0.1140.2790.137-0.0690.4690.116-0.025-0.2390.398-0.062-0.062-0.019-0.019-0.040-0.0040.0080.0920.0940.059-0.150-0.1500.1420.0660.1710.5460.2630.4040.551-0.4180.2970.2500.6160.813-0.105-0.0190.1330.3280.1760.4391.0000.0010.148-0.202-0.032-0.008-0.064-0.531-0.5430.095-0.4960.162-0.1910.1430.0770.1980.3070.2830.2610.2780.4270.3470.3010.467-0.0460.7510.000-0.1750.0160.1760.2160.0440.2400.0000.0770.0820.0000.0370.0750.0460.0000.0210.0000.0380.0001.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.000
avg_cur_bal0.2820.2820.282-0.0880.2480.4410.0570.0690.1360.136-0.017-0.0740.0390.017-0.0950.2850.1790.2140.0310.0310.2040.2040.1880.1490.023-0.019-0.0180.1830.0980.098-0.094-0.0450.927-0.0020.2040.1410.191-0.1430.3910.081-0.145-0.1900.2720.2300.1920.1300.1760.1230.0011.0000.0280.1130.0180.1920.1970.1390.0030.6360.134-0.034-0.021-0.0130.000-0.108-0.130-0.1070.0010.261-0.158-0.017-0.1290.0160.0370.011-0.0460.091-0.0360.8770.4820.1610.3830.0900.0300.0170.0220.1780.0470.0000.0000.0630.0280.0560.0001.0000.0000.0000.0890.0000.0000.0000.0000.0000.0000.0000.000
bc_open_to_buy0.2200.2200.221-0.3860.1770.222-0.056-0.0710.5010.5010.0200.0200.0060.334-0.0730.201-0.6100.2360.1430.1430.0700.0700.096-0.053-0.053-0.085-0.0820.1080.3220.3220.033-0.0430.1460.121-0.0260.0300.027-0.0340.035-0.0480.1460.1640.213-0.5300.6890.0340.0230.0370.1480.0281.000-0.746-0.0050.0190.177-0.126-0.1000.113-0.2400.006-0.0390.005-0.1290.2930.1330.5410.4310.0060.3950.3240.1370.336-0.0610.1330.177-0.638-0.0130.2730.1290.7740.0520.0100.1330.1140.0000.0360.0710.0410.0000.0150.0000.0800.0231.0000.0000.0000.0080.0620.0000.0000.0000.0210.0000.0510.099
bc_util0.0440.0440.0440.3200.0740.0050.1820.025-0.441-0.441-0.085-0.0310.024-0.117-0.0400.3450.864-0.086-0.086-0.0860.1210.1210.0780.2220.0320.0680.0640.008-0.229-0.229-0.011-0.0840.059-0.2020.055-0.082-0.0670.0870.010-0.066-0.173-0.1830.3840.608-0.232-0.109-0.047-0.119-0.2020.113-0.7461.000-0.0060.0550.0090.1730.1770.0180.223-0.0200.109-0.0350.0130.0700.126-0.196-0.151-0.000-0.166-0.1230.126-0.1180.006-0.195-0.0520.8640.000-0.0400.118-0.2510.0180.0420.1340.1190.0200.0290.1070.0430.0000.0600.0600.1020.0461.0000.0210.0000.0390.0000.0000.0000.0000.0110.0000.0300.031
delinq_amnt-0.023-0.023-0.0230.005-0.0240.0220.0250.110-0.019-0.019-0.030-0.128NaN0.010-0.0240.0180.0060.009-0.019-0.019-0.013-0.013-0.010-0.011-0.012-0.017-0.017-0.007-0.031-0.031-0.0840.0060.019-0.0300.032-0.002-0.0210.0060.020-0.029-0.009-0.032-0.0130.0180.015-0.018-0.020-0.020-0.0320.018-0.005-0.0061.0000.0230.0330.0040.0120.0240.017-0.0740.014-0.077-0.0050.0040.003-0.0100.0020.010-0.0060.0050.003-0.0030.035-0.004-0.0440.009-0.0090.0250.0280.0020.0360.0000.0000.0000.0000.0000.0150.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.4250.0000.0000.3380.0000.0000.0000.065
mo_sin_old_il_acct0.1070.1070.107-0.1040.0910.2160.0430.1000.0280.0280.004-0.036-0.1490.1350.0530.1630.0670.3530.0230.0230.0810.0810.0770.0560.0110.0010.0050.0350.0480.048-0.0240.0160.2250.0140.1850.0280.046-0.0050.215-0.030-0.049-0.0470.1390.0570.1290.0060.1160.011-0.0080.1920.0190.0550.0231.0000.2880.0350.0160.2090.043-0.004-0.007-0.0210.1100.0270.0500.0340.1270.4330.0480.1480.0460.1330.045-0.007-0.1430.0590.0450.2310.2470.1000.2270.0380.0360.0420.0460.0870.0110.0000.0050.0000.0200.0000.0001.0000.0000.0000.0000.0000.0280.0000.0000.0000.0050.0000.000
mo_sin_old_rev_tl_op0.2070.2070.207-0.1720.1770.2480.0700.1320.1410.141-0.027-0.092-0.1740.1840.0360.3280.0300.3590.0060.0060.1870.1870.1860.1100.005-0.023-0.0190.1010.1630.163-0.001-0.0300.237-0.024-0.020-0.032-0.0090.0550.017-0.145-0.054-0.0900.263-0.0620.351-0.0360.090-0.046-0.0640.1970.1770.0090.0330.2881.0000.0350.0310.3700.068-0.007-0.002-0.0410.0910.1270.1620.1730.3740.0490.2140.4220.1640.1810.055-0.043-0.1420.0320.0040.2840.1550.3070.0460.0730.0550.0370.0870.1350.0450.0330.0000.0000.0000.0260.0441.0000.0330.0000.0000.0320.0720.0000.0120.0000.0000.0000.000
mo_sin_rcnt_rev_tl_op0.0540.0540.053-0.1180.0370.0310.0040.0300.1130.113-0.309-0.0650.021-0.276-0.0940.0350.207-0.1760.0730.0730.0120.0120.0210.000-0.012-0.060-0.064-0.0030.0960.096-0.046-0.0490.028-0.6710.016-0.034-0.0280.0300.010-0.068-0.830-0.6870.1110.152-0.089-0.072-0.018-0.268-0.5310.139-0.1260.1730.0040.0350.0351.0000.7580.0080.704-0.0740.232-0.068-0.074-0.170-0.268-0.224-0.195-0.003-0.347-0.271-0.262-0.2740.015-0.6480.0270.150-0.0240.0120.034-0.0280.0200.0000.0090.0000.0000.0100.0300.0000.0000.0030.0000.0310.0001.0000.0060.0000.0160.0180.0000.0490.0270.0000.0000.0000.000
mo_sin_rcnt_tl0.0430.0430.043-0.1330.027-0.056-0.0470.0180.0750.075-0.339-0.0610.004-0.252-0.0830.0800.207-0.2100.0660.0660.0250.0250.0370.013-0.018-0.081-0.082-0.0130.1080.108-0.054-0.022-0.090-0.848-0.135-0.366-0.2710.418-0.163-0.232-0.588-0.4750.1260.058-0.047-0.166-0.070-0.355-0.5430.003-0.1000.1770.0120.0160.0310.7581.000-0.0590.509-0.0620.328-0.075-0.069-0.074-0.152-0.131-0.125-0.147-0.231-0.188-0.145-0.2500.010-0.7100.0330.154-0.016-0.090-0.0950.010-0.1200.0140.0410.0130.0160.0170.0450.0060.0000.0270.0000.0250.0001.0000.0000.0000.0230.0320.0000.0480.0000.0000.0000.0000.000
mort_acc0.2450.2450.245-0.1180.2100.345-0.0050.1030.1030.1030.025-0.068-0.0900.183-0.0110.2680.0410.416-0.030-0.0300.2240.2240.2160.142-0.018-0.003-0.0010.1870.1170.117-0.068-0.0400.6560.0710.0600.0730.107-0.0510.123-0.042-0.008-0.0260.2050.0190.2780.1020.1960.1300.0950.6360.1130.0180.0240.2090.3700.008-0.0591.0000.041-0.059-0.043-0.0500.0900.0250.0610.0780.2280.1800.1170.2750.0600.1800.0580.081-0.1020.023-0.0230.6650.2280.2120.1460.1030.0500.0520.0730.2920.0460.0280.0000.0140.0520.0000.0941.0000.0980.0000.0560.1600.0000.4990.0400.0200.0000.0000.000
mths_since_recent_bc0.0320.0320.032-0.0930.0150.007-0.0030.0430.0730.073-0.219-0.0720.014-0.264-0.041-0.0030.188-0.1510.0390.0390.0150.0150.0250.003-0.031-0.066-0.069-0.0120.0910.091-0.038-0.0370.026-0.4900.001-0.045-0.0460.053-0.011-0.066-0.683-0.6310.0750.145-0.124-0.086-0.007-0.224-0.4960.134-0.2400.2230.0170.0430.0680.7040.5090.0411.000-0.0760.137-0.071-0.064-0.311-0.264-0.378-0.277-0.011-0.329-0.236-0.258-0.2650.010-0.5370.0090.186-0.0130.0070.004-0.1420.0030.0300.0090.0000.0000.0090.0470.0300.0000.0090.0000.0360.0001.0000.0640.0000.0300.0560.0300.1360.0300.0120.0000.0000.020
mths_since_recent_bc_dlq-0.057-0.057-0.056-0.053-0.062-0.0470.015-0.6500.0680.0680.0680.764-0.161-0.0150.103-0.020-0.0260.005-0.008-0.008-0.042-0.042-0.033-0.059-0.0990.0090.009-0.0380.0740.0740.5640.062-0.0400.073-0.0230.0940.142-0.1060.0240.0800.0850.188-0.0230.004-0.0080.1200.0290.0740.162-0.0340.006-0.020-0.074-0.004-0.007-0.074-0.062-0.059-0.0761.000-0.0130.8990.239-0.0030.0030.0010.053-0.0120.0120.028-0.000-0.013-0.2220.1210.0940.005-0.004-0.051-0.005-0.019-0.0130.0330.0000.0000.0280.0640.0000.0220.0000.0000.0000.0000.0001.0000.0000.0620.0980.1850.0981.0000.1520.0750.0000.0000.039
mths_since_recent_inq0.0070.0070.008-0.144-0.004-0.0640.018-0.0420.0610.061-0.6870.0140.033-0.089-0.0620.0800.114-0.1130.0340.0340.0040.0040.020-0.011-0.014-0.049-0.052-0.0390.0880.0880.006-0.024-0.053-0.320-0.030-0.165-0.1160.179-0.060-0.094-0.203-0.1620.1040.0360.015-0.207-0.054-0.662-0.191-0.021-0.0390.1090.014-0.007-0.0020.2320.328-0.0430.137-0.0131.000-0.014-0.067-0.000-0.028-0.040-0.071-0.085-0.079-0.086-0.025-0.087-0.043-0.2780.0570.1050.002-0.052-0.0400.036-0.0550.0000.0550.0350.0230.0390.0400.0080.0000.0000.0000.0110.0001.0000.0000.0000.0320.0000.0000.0300.0000.0350.0000.0320.011
mths_since_recent_revol_delinq-0.031-0.031-0.031-0.047-0.035-0.0460.011-0.7100.0730.0730.0530.848-0.128-0.0420.089-0.057-0.013-0.0120.0190.019-0.049-0.049-0.035-0.070-0.071-0.010-0.007-0.0170.0800.0800.5580.072-0.0320.059-0.0150.0770.086-0.0900.0270.0640.0660.115-0.0360.018-0.0540.062-0.0160.0230.143-0.0130.005-0.035-0.077-0.021-0.041-0.068-0.075-0.050-0.0710.899-0.0141.0000.2350.018-0.0180.0120.0330.014-0.040-0.029-0.023-0.041-0.2040.1250.095-0.025-0.024-0.047-0.001-0.0380.0170.0000.0000.0000.0430.0390.0000.0000.0000.0000.0050.0390.0001.0000.0000.0390.0730.1750.0781.0000.1510.0580.0000.0730.000
num_accts_ever_120_pd-0.073-0.073-0.0740.078-0.0610.052-0.0770.209-0.293-0.2930.0580.089-0.173-0.0030.001-0.1470.0060.119-0.031-0.031-0.039-0.039-0.041-0.0180.0390.0500.055-0.019-0.129-0.129-0.0330.119-0.0000.0800.0580.0650.043-0.0340.0630.0710.0710.081-0.1460.084-0.1930.0750.0040.1060.0770.000-0.1290.013-0.0050.1100.091-0.074-0.0690.090-0.0640.239-0.0670.2351.000-0.0330.002-0.0730.0580.091-0.0230.0660.006-0.0040.3370.084-0.5920.0020.028-0.023-0.009-0.1950.0380.0320.0150.0360.0250.0000.0000.0000.0200.0100.0590.0000.0001.0000.0000.0000.0000.0000.1100.0000.0000.0000.0200.0000.000
num_actv_bc_tl0.1910.1910.1910.0340.1960.1840.168-0.025-0.089-0.0890.0330.027-0.0120.543-0.0660.4920.1330.2860.0330.0330.1520.1520.1240.175-0.0090.0230.0250.075-0.043-0.0430.072-0.0460.0990.0780.038-0.039-0.0250.0440.038-0.0800.2260.2980.3110.0080.4390.004-0.0920.0320.198-0.1080.2930.0700.0040.0270.127-0.170-0.0740.025-0.311-0.003-0.0000.018-0.0331.0000.8000.8360.595-0.0200.6390.4400.7950.544-0.0350.1310.0760.0830.0190.1320.1960.5220.0380.0410.0000.0260.0260.0190.0150.0260.0000.0300.0040.0270.0001.0000.0000.0000.0450.0000.0000.0000.0000.0180.0000.0250.000
num_actv_rev_tl0.1530.1530.1530.1080.1620.1370.2510.026-0.166-0.1660.078-0.011-0.0060.668-0.0150.5030.1330.3960.0010.0010.1440.1440.1070.1930.0010.0430.0460.058-0.104-0.1040.017-0.0160.1190.1530.056-0.033-0.0060.0400.050-0.0710.3190.4120.2080.0170.4360.0080.0120.0840.307-0.1300.1330.1260.0030.0500.162-0.268-0.1520.061-0.2640.003-0.028-0.0180.0020.8001.0000.6540.4990.0160.7840.5770.9900.6670.0070.2220.0240.1380.0240.1460.2000.3260.0390.0440.0630.0990.0520.0330.0390.0600.0750.0360.0100.0430.0001.0000.0000.0000.0360.0000.0000.0000.0000.0000.0750.0000.031
num_bc_sats0.2220.2220.222-0.0690.2140.2250.114-0.0360.0770.0770.0780.030-0.0050.648-0.0580.425-0.1150.3930.0430.0430.1500.1500.1350.119-0.027-0.002-0.0000.1140.0540.0540.093-0.0480.1320.1620.0400.0130.029-0.0120.064-0.0660.3010.3690.291-0.1710.5790.038-0.0460.0950.283-0.1070.541-0.196-0.0100.0340.173-0.224-0.1310.078-0.3780.001-0.0400.012-0.0730.8360.6541.0000.7400.0160.7580.5680.6480.649-0.0510.2100.113-0.1370.0100.2020.2000.6570.0620.0530.0190.0000.0290.0270.0000.1050.0000.0190.0260.0000.0001.0000.0000.0190.0460.0000.0000.0000.0110.0000.0000.0150.000
num_bc_tl0.2220.2220.222-0.1130.2110.2540.0910.0400.0490.0490.112-0.002-0.1880.5580.0130.383-0.0980.625-0.071-0.0710.2190.2190.2120.129-0.0200.0210.0220.1600.0570.0570.044-0.0530.1910.1540.0400.0210.036-0.0010.074-0.0770.2450.3140.277-0.1720.5120.0460.0420.1070.2610.0010.431-0.1510.0020.1270.374-0.195-0.1250.228-0.2770.053-0.0710.0330.0580.5950.4990.7401.0000.0870.6450.8250.4990.5560.0140.191-0.013-0.0940.0210.2570.1940.5360.0740.0450.0400.0140.0360.0750.0020.0620.0000.0110.0000.0250.0651.0000.0290.0000.0660.0000.0580.0000.0000.0000.0000.0370.029
num_il_tl0.1100.1100.1100.0010.0950.2700.2710.0780.0240.0240.092-0.026-0.0540.388-0.0100.0750.0130.662-0.015-0.0150.0810.0810.0680.0630.0210.0300.0330.103-0.027-0.027-0.0890.0170.3770.1490.6780.3640.519-0.3670.6330.181-0.013-0.0100.0450.2140.0750.2160.3230.1930.2780.2610.006-0.0000.0100.4330.049-0.003-0.1470.180-0.011-0.012-0.0850.0140.091-0.0200.0160.0160.0871.0000.0670.1330.0150.3870.0530.210-0.0630.001-0.0190.3580.5730.0240.6380.0800.0080.0060.0170.0810.0230.0520.0000.0280.0700.0370.0001.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0180.000
num_op_rev_tl0.1780.1780.178-0.0260.1740.1660.1860.0210.0330.0330.129-0.0210.0240.8490.0070.404-0.2000.567-0.001-0.0010.1460.1460.1290.122-0.0270.0190.0230.1050.0110.0110.047-0.0020.1430.2520.0530.0110.046-0.0040.063-0.0610.4100.5160.176-0.2210.5950.0620.0760.1470.427-0.1580.395-0.166-0.0060.0480.214-0.347-0.2310.117-0.3290.012-0.079-0.040-0.0230.6390.7840.7580.6450.0671.0000.7890.7840.849-0.0200.3180.069-0.1140.0020.2150.1920.4630.0660.0450.0000.0310.0330.0600.0250.0000.0160.0260.0000.0000.0001.0000.0000.0000.0290.0000.0000.0000.0000.0000.0160.0120.016
num_rev_accts0.2020.2020.202-0.0820.1930.2130.1510.0960.0240.0240.139-0.060-0.1480.6860.0480.373-0.1530.766-0.073-0.0730.2090.2090.1990.134-0.0210.0240.0270.1550.0360.036-0.000-0.0200.2120.2160.0580.0240.0460.0020.080-0.0770.3100.3980.194-0.1960.5270.0440.1430.1410.347-0.0170.324-0.1230.0050.1480.422-0.271-0.1880.275-0.2360.028-0.086-0.0290.0660.4400.5770.5680.8250.1330.7891.0000.5760.6830.0360.259-0.048-0.0720.0060.2810.1980.3950.0860.0520.0360.0390.0520.0950.0170.0460.0130.0180.0000.0270.0001.0000.0000.0000.0580.0000.0250.0000.0000.0000.0130.0000.015
num_rev_tl_bal_gt_00.1510.1510.1510.1030.1590.1360.2490.024-0.160-0.1600.067-0.018-0.0010.668-0.0200.5040.1350.395-0.000-0.0000.1410.1410.1050.1890.0020.0420.0450.054-0.104-0.1040.025-0.0180.1200.1460.056-0.033-0.0060.0450.048-0.0750.3140.4070.2060.0230.4360.0150.0180.0800.301-0.1290.1370.1260.0030.0460.164-0.262-0.1450.060-0.258-0.000-0.025-0.0230.0060.7950.9900.6480.4990.0150.7840.5761.0000.668-0.0030.2180.0270.1390.0260.1470.2010.3270.0390.0400.0660.1000.0460.0340.0160.0400.0500.0310.0000.0500.0001.0000.0000.0000.0430.0000.0000.0000.0000.0000.0500.0000.028
num_sats0.2010.2010.201-0.0230.1920.2720.3070.0580.0660.0660.135-0.0520.0460.999-0.0380.393-0.1330.7150.0030.0030.1580.1580.1350.142-0.0080.0280.0330.1220.0060.006-0.019-0.0040.3580.2760.4580.1720.247-0.1630.3730.0510.3310.4140.195-0.0410.5420.1350.1500.1940.4670.0160.336-0.118-0.0030.1330.181-0.274-0.2500.180-0.265-0.013-0.087-0.041-0.0040.5440.6670.6490.5560.3870.8490.6830.6681.0000.0070.3530.043-0.078-0.0190.4090.4720.4230.3880.0770.0110.0260.0000.0840.0080.0000.0000.0160.0000.0210.0001.0000.0000.0000.0150.0000.0000.0000.0000.0080.0000.0000.000
num_tl_90g_dpd_24m-0.018-0.018-0.0180.058-0.0160.022-0.0140.518-0.150-0.1500.031-0.356-0.0390.009-0.036-0.0620.0020.066-0.006-0.006-0.005-0.005-0.0180.0300.0510.0260.028-0.026-0.084-0.084-0.6910.0120.0340.0190.0370.012-0.0350.0070.0510.013-0.010-0.033-0.0820.029-0.079-0.022-0.0010.036-0.0460.037-0.0610.0060.0350.0450.0550.0150.0100.0580.010-0.222-0.043-0.2040.337-0.0350.007-0.0510.0140.053-0.0200.036-0.0030.0071.000-0.024-0.265-0.003-0.0140.0260.017-0.0980.0410.0180.0000.0000.0030.0000.0000.0460.0000.0000.0000.0000.0001.0000.0470.0000.0000.0000.2970.0000.0000.0000.0000.0000.028
num_tl_op_past_12m-0.036-0.036-0.0370.173-0.0150.0830.100-0.042-0.099-0.0990.3300.105-0.0060.3540.101-0.053-0.2230.299-0.064-0.064-0.029-0.029-0.046-0.0080.0180.0880.0900.029-0.143-0.1430.0690.0500.1420.7140.2000.5400.403-0.4590.2330.2840.8040.632-0.105-0.0190.0920.2600.1200.5110.7510.0110.133-0.195-0.004-0.007-0.043-0.648-0.7100.081-0.5370.121-0.2780.1250.0840.1310.2220.2100.1910.2100.3180.2590.2180.353-0.0241.000-0.020-0.1760.0190.1430.1620.0230.1760.0000.0770.0890.0000.0260.0790.0330.0000.0130.0000.0240.0001.0000.0000.0000.0000.0000.0360.0000.0000.0530.0000.0000.000
pct_tl_nvr_dlq0.0710.0710.072-0.0930.050-0.0700.097-0.4890.3700.370-0.0320.1940.1960.0400.0190.146-0.050-0.0800.0370.0370.0190.0190.025-0.007-0.060-0.038-0.0420.0380.1860.1860.134-0.148-0.029-0.049-0.058-0.011-0.001-0.010-0.041-0.020-0.028-0.0170.130-0.0900.223-0.042-0.012-0.0770.000-0.0460.177-0.052-0.044-0.143-0.1420.0270.033-0.1020.0090.0940.0570.095-0.5920.0760.0240.113-0.013-0.0630.069-0.0480.0270.043-0.265-0.0201.000-0.033-0.054-0.0080.0300.230-0.0260.0580.0490.0340.0000.0000.0180.0100.0760.0670.0640.0430.0001.0000.0000.0380.0000.0350.0720.0000.0610.0030.0760.0310.019
percent_bc_gt_750.0220.0220.0220.3010.050-0.0120.1710.012-0.402-0.402-0.075-0.0130.015-0.078-0.0390.3110.748-0.051-0.100-0.1000.1090.1090.0700.1970.0270.0760.0690.011-0.219-0.2190.026-0.0810.053-0.1650.071-0.080-0.0590.0780.015-0.075-0.145-0.1450.3060.514-0.174-0.104-0.027-0.115-0.1750.091-0.6380.8640.0090.0590.0320.1500.1540.0230.1860.0050.105-0.0250.0020.0830.138-0.137-0.0940.001-0.114-0.0720.139-0.078-0.003-0.176-0.0331.000-0.002-0.0280.101-0.1930.0140.0440.1260.1110.0140.0220.0860.0490.0000.0440.0230.0980.0611.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.028
tax_liens0.0000.0000.0000.0170.0090.036-0.0340.014-0.087-0.0870.0170.018-0.346-0.0190.418-0.014-0.003-0.018-0.032-0.0320.0380.0380.0370.0320.070-0.002-0.0010.005-0.032-0.0320.0160.037-0.0400.026-0.020-0.011-0.0170.009-0.024-0.0250.0450.045-0.026-0.008-0.0200.035-0.0390.0180.016-0.036-0.0130.000-0.0090.0450.004-0.024-0.016-0.023-0.013-0.0040.002-0.0240.0280.0190.0240.0100.021-0.0190.0020.0060.026-0.019-0.0140.019-0.054-0.0021.000-0.035-0.035-0.019-0.0260.0000.0190.0320.0000.0000.0280.0000.0000.0000.0520.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0350.0000.0000.000
tot_hi_cred_lim0.3620.3620.362-0.1600.3210.5480.1530.0870.2400.2400.032-0.1000.0510.410-0.1060.4220.0100.4790.0700.0700.2390.2390.2220.1670.014-0.031-0.0270.2160.1500.150-0.080-0.0440.9710.1040.3350.1680.240-0.1680.456-0.0030.007-0.0010.3370.0450.4860.1530.2150.1720.1760.8770.273-0.0400.0250.2310.2840.012-0.0900.6650.007-0.051-0.052-0.047-0.0230.1320.1460.2020.2570.3580.2150.2810.1470.4090.0260.143-0.008-0.028-0.0351.0000.5920.4080.4890.1180.0440.0230.0550.3020.0520.0280.0000.0540.0000.0540.0001.0000.0000.0000.0780.0000.0000.0000.0000.0260.0000.0000.000
total_bal_ex_mort0.3230.3230.3230.0240.3030.4910.4530.0420.0730.0730.018-0.044-0.0090.471-0.1060.4700.1780.4850.0520.0520.2240.2240.1860.2230.0410.0140.0170.1870.0080.008-0.047-0.0600.6180.0870.6470.3280.434-0.3480.8720.233-0.0210.0010.3750.3720.3850.1760.2090.1710.2160.4820.1290.1180.0280.2470.1550.034-0.0950.2280.004-0.005-0.040-0.001-0.0090.1960.2000.2000.1940.5730.1920.1980.2010.4720.0170.1620.0300.101-0.0350.5921.0000.3280.8620.1150.0080.0000.0200.1040.0500.0260.0000.0000.0200.0250.0001.0000.0000.0000.0680.0000.0000.0000.0000.0230.0000.0310.000
total_bc_limit0.3990.3990.399-0.2860.3600.3820.086-0.0850.3830.383-0.037-0.0000.0210.420-0.1640.648-0.1430.3060.1440.1440.2290.2290.2260.132-0.057-0.070-0.0700.1900.2860.2860.068-0.1480.2990.0140.018-0.027-0.0210.0300.072-0.1510.0720.0850.653-0.2750.881-0.042-0.006-0.0400.0440.1610.774-0.2510.0020.1000.307-0.0280.0100.212-0.142-0.0190.036-0.038-0.1950.5220.3260.6570.5360.0240.4630.3950.3270.423-0.0980.0230.230-0.193-0.0190.4080.3281.0000.1150.0740.1100.0890.0150.0780.0200.0360.0000.0000.0520.0650.0001.0000.0740.0000.0000.0000.0000.0000.0000.0660.0000.0490.040
total_il_high_credit_limit0.1870.1870.1870.0070.1720.4070.4670.0650.0900.0900.031-0.043-0.0060.388-0.0380.1380.0370.4210.0430.0430.1140.1140.0910.1150.0400.0050.0100.1180.0020.002-0.0510.0040.4970.1040.7880.3530.500-0.3920.9540.130-0.027-0.0130.1380.2380.1390.2280.2250.1920.2400.3830.0520.0180.0360.2270.0460.020-0.1200.1460.003-0.013-0.0550.0170.0380.0380.0390.0620.0740.6380.0660.0860.0390.3880.0410.176-0.0260.014-0.0260.4890.8620.1151.0000.0870.0010.0000.0240.1080.0430.0000.0000.0320.0230.0410.0001.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0150.000
term0.4370.4370.4370.3710.3170.0000.0250.0120.0540.0520.0190.0000.0000.0810.0000.0670.0720.0990.2950.2950.2120.2120.1180.5030.0430.1550.1490.1970.0660.0730.0580.0010.1170.0000.0480.0000.0150.0000.0880.0000.0000.0000.0710.0370.0570.0340.0630.0260.0000.0900.0100.0420.0000.0380.0730.0000.0140.1030.0300.0330.0000.0000.0320.0410.0440.0530.0450.0800.0450.0520.0400.0770.0180.0000.0580.0440.0000.1180.1150.0740.0871.0000.4000.4020.0650.1100.0980.2010.0000.0930.0270.1430.0950.0000.0720.0070.0760.0000.0120.0000.0000.0010.0000.0000.029
grade0.0700.0690.0690.7000.0790.0000.0000.0000.1940.1940.0830.0000.0000.0110.0260.0000.1280.0260.0280.0280.0810.0810.0490.2160.0610.0960.0920.0580.1780.1790.0000.0000.0250.0620.0000.0840.0790.0000.0150.0560.0400.0590.0000.1390.0930.0700.0000.0770.0770.0300.1330.1340.0000.0360.0550.0090.0410.0500.0090.0000.0550.0000.0150.0000.0630.0190.0400.0080.0000.0360.0660.0110.0000.0770.0490.1260.0190.0440.0080.1100.0010.4001.0000.9970.0000.0490.1950.1060.0000.0710.0550.1450.0630.0000.0690.0230.0170.0000.0000.0000.0000.0410.0000.1750.065
sub_grade0.0680.0660.0670.7340.0880.0000.0240.0000.1750.1750.0960.0420.0000.0110.0320.0000.1110.0390.0280.0280.1100.1100.0510.2350.0920.1130.1100.0660.1580.1980.0000.0000.0000.0810.0000.1210.1140.0000.0000.0480.0670.0660.0000.1230.0680.0890.0000.1150.0820.0170.1140.1190.0000.0420.0370.0000.0130.0520.0000.0000.0350.0000.0360.0260.0990.0000.0140.0060.0310.0390.1000.0260.0000.0890.0340.1110.0320.0230.0000.0890.0000.4020.9971.0000.0000.0420.2060.1140.0080.0470.0400.1460.0620.0000.0630.0200.0490.0000.0000.0000.0340.0460.0080.1930.092
emp_length0.0320.0320.0320.0080.0320.0000.0270.0060.0180.0130.0000.0260.0600.0000.0080.0250.0120.0510.0100.0100.0370.0370.0240.0330.0000.0000.0000.0000.0220.0260.0230.0000.0590.0160.0380.0620.0310.0250.0280.0260.0000.0000.0420.0060.0280.0000.0000.0000.0000.0220.0000.0200.0000.0460.0870.0000.0160.0730.0000.0280.0230.0430.0250.0260.0520.0290.0360.0170.0330.0520.0460.0000.0030.0000.0000.0140.0000.0550.0200.0150.0240.0650.0000.0001.0000.1080.0320.0200.0350.0150.0000.0150.0430.0000.0000.0320.1280.0000.0000.0420.0000.0200.0350.0270.000
home_ownership0.1250.1250.1250.0470.0960.0430.0000.0000.0670.0650.0050.0280.0000.0850.0000.0910.0220.1520.0710.0710.0930.0940.0940.0580.0000.0000.0000.0630.0650.0650.0760.0120.2800.0000.0680.0000.0290.0270.1020.0400.0230.0000.0640.0230.1000.0410.0670.0900.0370.1780.0360.0290.0000.0870.1350.0100.0170.2920.0090.0640.0390.0390.0000.0190.0330.0270.0750.0810.0600.0950.0340.0840.0000.0260.0000.0220.0000.3020.1040.0780.1080.1100.0490.0420.1081.0000.0360.0260.0020.1020.1430.0480.0000.0000.0000.0000.0750.0000.0000.0000.0000.0000.0020.1070.000
verification_status0.1610.1610.1610.1970.1580.0640.0180.0050.1370.1360.0470.0000.0000.0050.0310.0460.1060.0260.0700.0700.1660.1660.1430.1700.0250.0600.0600.0690.1070.0940.0000.0000.0570.0530.0060.0430.0520.0190.0400.0990.0470.0630.0360.1310.0220.0240.0300.0610.0750.0470.0710.1070.0150.0110.0450.0300.0450.0460.0470.0000.0400.0000.0000.0150.0390.0000.0020.0230.0250.0170.0160.0080.0000.0790.0180.0860.0280.0520.0500.0200.0430.0980.1950.2060.0320.0361.0000.0810.0000.0640.0000.0800.0700.0000.0610.0200.0320.0000.0300.0140.0000.0000.0000.0250.000
loan_status0.0540.0540.0550.0990.0230.0510.0000.0350.0600.0590.0930.0000.0450.0000.0090.0270.0350.0390.3230.3230.1760.1750.2250.1050.1320.2370.2280.2420.3040.3220.0000.0000.0330.0370.0000.0270.0340.0090.0000.0370.0000.0200.0000.0560.0460.0560.0000.0550.0460.0000.0410.0430.0000.0000.0330.0000.0060.0280.0300.0220.0080.0000.0000.0260.0600.1050.0620.0520.0000.0460.0400.0000.0460.0330.0100.0490.0000.0280.0260.0360.0000.2010.1060.1140.0200.0260.0811.0000.2180.0340.0290.2420.4820.9990.4970.0000.1680.0370.0000.0000.0000.0000.2180.1870.354
pymnt_plan0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1190.0000.0000.0000.0000.0000.0610.0610.0000.0000.0000.0000.0510.0000.0000.0000.0460.0000.0000.2850.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0750.0000.0000.0000.0160.0130.0500.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0080.0350.0020.0000.2181.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.8330.0000.000
purpose0.0980.0980.0990.0610.0900.0010.0000.0000.0450.0470.0260.0000.0000.0110.0000.0000.0640.0180.0210.0210.0540.0540.0480.0000.0000.0000.0000.0100.0440.0440.0000.0000.0580.0000.0260.0000.0440.0000.0170.0000.0280.0000.0210.0370.0000.0680.0300.0090.0210.0630.0150.0600.0000.0000.0000.0030.0270.0140.0090.0000.0000.0000.0100.0300.0360.0190.0110.0280.0260.0180.0310.0160.0000.0130.0670.0440.0000.0540.0000.0000.0320.0930.0710.0470.0150.1020.0640.0340.0001.0000.0000.0590.2010.1110.2820.0000.0320.0000.0000.0000.0000.0000.0000.1240.042
addr_state0.0000.0000.0000.0350.0000.0000.0000.0800.0300.0280.0280.0000.0000.0000.0610.0000.0570.0320.0080.0080.0170.0180.0080.0000.0000.0000.0000.0000.0050.0270.0000.0000.0230.0000.0000.0970.0430.0270.0420.0000.0000.0000.0310.0110.0000.0000.1130.1030.0000.0280.0000.0600.0610.0200.0000.0000.0000.0520.0000.0000.0000.0050.0590.0040.0100.0260.0000.0700.0000.0000.0000.0000.0000.0000.0640.0230.0520.0000.0200.0520.0230.0270.0550.0400.0000.1430.0000.0290.0000.0001.0000.0000.0000.0000.0540.0000.0330.0000.0000.0350.0000.0570.0000.0890.000
initial_list_status0.0910.0910.0930.1440.0450.0000.0000.0000.1090.1090.0830.0000.0840.0290.0000.0000.0820.0000.2420.2420.1090.1080.1340.0590.0240.0080.0070.0780.1050.0920.0000.0000.0510.0370.0000.0350.0000.0000.0150.0490.0060.0340.0270.0440.0650.0000.0350.0390.0380.0560.0800.1020.0000.0000.0260.0310.0250.0000.0360.0000.0110.0390.0000.0270.0430.0000.0250.0370.0000.0270.0500.0210.0000.0240.0430.0980.0000.0540.0250.0650.0410.1430.1450.1460.0150.0480.0800.2420.0000.0590.0001.0000.3350.0000.2160.0000.0930.0000.0000.0000.0000.0000.0000.0870.033
last_pymnt_d0.0000.0000.0000.0410.0000.0600.0000.0000.0490.0460.1200.0000.0920.0000.0000.0000.0370.0000.2100.2100.1050.1050.1340.0000.0850.1120.1070.2000.1200.1530.0000.0000.0000.1890.0000.2110.1990.0720.0000.0000.0490.0450.1410.0390.0000.1050.0790.0780.0000.0000.0230.0460.0000.0000.0440.0000.0000.0940.0000.0000.0000.0000.0000.0000.0000.0000.0650.0000.0000.0000.0000.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0950.0630.0620.0430.0000.0700.4820.0000.2010.0000.3351.0000.9990.4240.0000.1350.0920.0660.0360.0100.0000.0000.1240.100
next_pymnt_d0.0240.0240.0240.0000.0000.0000.0000.0000.0000.0000.4970.0000.1460.0000.0250.0000.0410.0490.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.9990.0000.1110.0000.0000.9991.0000.9970.0000.0000.0000.0001.0001.0000.0000.0000.0000.000
last_credit_pull_d0.0000.0000.0000.0150.0000.0000.0000.0000.0610.0590.0910.0000.0540.0000.0000.0000.0280.0000.1190.1190.0890.0890.1110.0000.0000.0990.0890.1880.1320.1580.0000.0000.0000.0650.0000.0900.1110.0760.0000.0000.0000.0000.2580.0370.0780.0450.0560.0000.0000.0000.0000.0210.0000.0000.0330.0060.0000.0980.0640.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0740.0000.0720.0690.0630.0000.0000.0610.4970.0000.2820.0540.2160.4240.9971.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.251
collections_12_mths_ex_med0.0000.0000.0000.0070.0000.0000.0000.0000.0610.0610.0070.0000.0540.0000.0000.0000.0250.0000.0000.0000.0080.0080.0080.0000.0000.0360.0460.0000.0000.0080.1490.0570.0000.0000.0000.0000.0000.0000.0000.0410.0000.0370.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0620.0000.0390.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0380.0000.0090.0000.0000.0000.0000.0070.0230.0200.0320.0000.0200.0000.0000.0000.0000.0000.0000.0000.0001.0000.0150.0000.0400.0000.0000.0000.0000.0140.000
application_type0.1410.1410.1410.0370.1020.0300.2780.0000.1170.1170.0260.0430.0000.0100.0000.0000.0320.0000.2570.2570.0730.0730.0880.0500.0480.0000.0000.0680.0510.0550.0940.0000.0770.0000.0000.0000.0000.0000.0580.0000.0390.0460.0000.0000.0740.0180.0680.0000.0000.0890.0080.0390.0000.0000.0000.0160.0230.0560.0300.0980.0320.0730.0000.0450.0360.0460.0660.0000.0290.0580.0430.0150.0000.0000.0000.0260.0000.0780.0680.0000.0640.0760.0170.0490.1280.0750.0320.1680.0000.0320.0330.0930.1350.0000.0000.0151.0000.0000.0210.0000.0000.0000.0000.0050.013
acc_now_delinq0.0000.0000.0000.0150.0000.0000.0000.0380.0110.0110.0000.1961.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0450.0370.1990.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0620.0000.4250.0000.0320.0180.0320.1600.0560.1850.0000.1750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0920.0000.0000.0000.0001.0000.0000.2360.7380.0000.0000.0000.000
chargeoff_within_12_mths0.0180.0180.0160.0000.0000.0000.0000.1580.0270.0270.0000.0470.0000.0000.0070.0000.0000.0310.0000.0000.0100.0090.0320.0000.0000.0000.0000.0350.0400.0060.2070.0000.0000.0000.0460.0630.0000.0000.0000.0080.0260.0000.0000.0000.0000.0000.0880.0000.0000.0000.0000.0000.0000.0280.0720.0000.0000.0000.0300.0980.0000.0780.1100.0000.0000.0000.0580.0300.0000.0250.0000.0000.2970.0360.0720.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0660.0000.0000.0400.0210.0001.0000.0000.0000.0580.0000.0000.051
num_tl_120dpd_2m0.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0231.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0490.0000.0130.0000.1190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0480.4990.1361.0000.0301.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0140.0000.0000.0000.0350.0000.0361.0000.0000.0000.0000.2360.0001.0000.0000.0000.0000.0000.000
num_tl_30dpd0.0000.0000.0000.0000.0000.0000.0000.0460.0000.0000.0000.1561.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0331.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3380.0000.0120.0270.0000.0400.0300.1520.0000.1510.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0101.0000.0000.0000.0000.7380.0000.0001.0000.0000.0000.0000.000
pub_rec_bankruptcies0.0350.0350.0360.0260.0280.0000.0000.0000.0940.0940.0870.1060.2520.0000.6270.0250.0380.0210.0000.0000.0270.0270.0330.0000.0000.0000.0000.0000.0580.0540.0830.0000.0160.0670.0000.0000.0000.0000.0000.0000.0840.0980.0000.0000.0520.1930.0000.1330.0420.0000.0210.0110.0000.0000.0000.0000.0000.0200.0120.0750.0350.0580.0000.0180.0000.0000.0000.0000.0000.0000.0000.0080.0000.0530.0030.0000.0350.0260.0230.0660.0000.0010.0410.0460.0200.0000.0000.0000.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0580.0000.0001.0000.0000.0000.023
hardship_flag0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1190.0000.0000.0000.0000.0000.0610.0610.0000.0000.0000.0000.0510.0000.0000.0000.0460.0000.0000.2850.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0750.0000.0000.0000.0160.0130.0500.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0080.0350.0020.0000.2180.8330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
disbursement_method0.0910.0910.0910.1750.0540.0000.0000.0000.0060.0000.0150.0000.0950.0000.0000.0000.0340.0000.2410.2410.1740.1740.1680.0840.0000.0000.0000.0740.0750.0680.0000.0000.0000.0000.0430.0000.0000.0480.0760.0820.0000.0000.0890.0000.0360.0080.0000.0000.0000.0000.0510.0300.0000.0000.0000.0000.0000.0000.0000.0000.0320.0730.0000.0250.0000.0150.0370.0180.0120.0000.0000.0000.0000.0000.0310.0000.0000.0000.0310.0490.0150.0000.1750.1930.0270.1070.0250.1870.0000.1240.0890.0870.1240.0000.0000.0140.0050.0000.0000.0000.0000.0000.0001.0000.016
debt_settlement_flag0.0440.0440.0410.0630.0000.0000.0000.0000.0000.0040.0000.0380.0000.0000.0000.0000.0270.0000.0820.0820.0330.0330.0480.0520.1240.4280.4340.0620.2140.2080.0300.0000.0000.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0990.0310.0650.0000.0000.0000.0000.0000.0200.0390.0110.0000.0000.0000.0310.0000.0290.0000.0160.0150.0280.0000.0280.0000.0190.0280.0000.0000.0000.0400.0000.0290.0650.0920.0000.0000.0000.3540.0000.0420.0000.0330.1000.0000.2510.0000.0130.0000.0510.0000.0000.0230.0000.0161.000

Missing values

2023-07-21T13:17:31.358443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.